{
"cells": [
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"source": [
"Last updated: 15 Feb 2023\n",
"\n",
"# 👋 PyCaret Regression Tutorial\n",
"\n",
"PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that exponentially speeds up the experiment cycle and makes you more productive.\n",
"\n",
"Compared with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks, such as scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and a few more.\n",
"\n",
"The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8116e19d",
"metadata": {},
"source": [
"# 💻 Installation\n",
"\n",
"PyCaret is tested and supported on the following 64-bit systems:\n",
"- Python 3.7 – 3.10\n",
"- Python 3.9 for Ubuntu only\n",
"- Ubuntu 16.04 or later\n",
"- Windows 7 or later\n",
"\n",
"You can install PyCaret with Python's pip package manager:\n",
"\n",
"`pip install pycaret`\n",
"\n",
"PyCaret's default installation will not install all the extra dependencies automatically. For that you will have to install the full version:\n",
"\n",
"`pip install pycaret[full]`\n",
"\n",
"or depending on your use-case you may install one of the following variant:\n",
"\n",
"- `pip install pycaret[analysis]`\n",
"- `pip install pycaret[models]`\n",
"- `pip install pycaret[tuner]`\n",
"- `pip install pycaret[mlops]`\n",
"- `pip install pycaret[parallel]`\n",
"- `pip install pycaret[test]`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d7142a33",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'3.0.0'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check installed version (must be >3.0)\n",
"import pycaret\n",
"pycaret.__version__"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "fb66e98d",
"metadata": {},
"source": [
"# 🚀 Quick start"
]
},
{
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"cell_type": "markdown",
"id": "00347d44",
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"source": [
"PyCaret's Regression Module is a supervised machine learning module that is used for estimating the relationships between a dependent variable (often called the outcome variable, or target) and one or more independent variables (often called features, predictors, or covariates). \n",
"\n",
"The objective of regression is to predict continuous values such as predicting sales amount, predicting quantity, predicting temperature, etc. Regression module provides several pre-processing features to preprocess the data for modeling through the setup function. \n",
"\n",
"PyCaret's regression module has many preprocessing capabilities and it coems with over 25 ready-to-use algorithms and several plots to analyze the performance of trained models. \n",
"\n",
"A typical workflow in PyCaret Regression module consist of the following 5 steps in this order:\n",
"\n",
"### **Setup** ➡️ **Compare Models** ➡️ **Analyze Model** ➡️ **Prediction** ➡️ **Save Model** \n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1b09f8df",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n", " | age | \n", "sex | \n", "bmi | \n", "children | \n", "smoker | \n", "region | \n", "charges | \n", "
---|---|---|---|---|---|---|---|
0 | \n", "19 | \n", "female | \n", "27.900 | \n", "0 | \n", "yes | \n", "southwest | \n", "16884.92400 | \n", "
1 | \n", "18 | \n", "male | \n", "33.770 | \n", "1 | \n", "no | \n", "southeast | \n", "1725.55230 | \n", "
2 | \n", "28 | \n", "male | \n", "33.000 | \n", "3 | \n", "no | \n", "southeast | \n", "4449.46200 | \n", "
3 | \n", "33 | \n", "male | \n", "22.705 | \n", "0 | \n", "no | \n", "northwest | \n", "21984.47061 | \n", "
4 | \n", "32 | \n", "male | \n", "28.880 | \n", "0 | \n", "no | \n", "northwest | \n", "3866.85520 | \n", "
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "Session id | \n", "123 | \n", "
1 | \n", "Target | \n", "charges | \n", "
2 | \n", "Target type | \n", "Regression | \n", "
3 | \n", "Original data shape | \n", "(1338, 7) | \n", "
4 | \n", "Transformed data shape | \n", "(1338, 10) | \n", "
5 | \n", "Transformed train set shape | \n", "(936, 10) | \n", "
6 | \n", "Transformed test set shape | \n", "(402, 10) | \n", "
7 | \n", "Ordinal features | \n", "2 | \n", "
8 | \n", "Numeric features | \n", "3 | \n", "
9 | \n", "Categorical features | \n", "3 | \n", "
10 | \n", "Preprocess | \n", "True | \n", "
11 | \n", "Imputation type | \n", "simple | \n", "
12 | \n", "Numeric imputation | \n", "mean | \n", "
13 | \n", "Categorical imputation | \n", "mode | \n", "
14 | \n", "Maximum one-hot encoding | \n", "25 | \n", "
15 | \n", "Encoding method | \n", "None | \n", "
16 | \n", "Fold Generator | \n", "KFold | \n", "
17 | \n", "Fold Number | \n", "10 | \n", "
18 | \n", "CPU Jobs | \n", "-1 | \n", "
19 | \n", "Use GPU | \n", "False | \n", "
20 | \n", "Log Experiment | \n", "False | \n", "
21 | \n", "Experiment Name | \n", "reg-default-name | \n", "
22 | \n", "USI | \n", "9f1c | \n", "
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "Session id | \n", "123 | \n", "
1 | \n", "Target | \n", "charges | \n", "
2 | \n", "Target type | \n", "Regression | \n", "
3 | \n", "Original data shape | \n", "(1338, 7) | \n", "
4 | \n", "Transformed data shape | \n", "(1338, 10) | \n", "
5 | \n", "Transformed train set shape | \n", "(936, 10) | \n", "
6 | \n", "Transformed test set shape | \n", "(402, 10) | \n", "
7 | \n", "Ordinal features | \n", "2 | \n", "
8 | \n", "Numeric features | \n", "3 | \n", "
9 | \n", "Categorical features | \n", "3 | \n", "
10 | \n", "Preprocess | \n", "True | \n", "
11 | \n", "Imputation type | \n", "simple | \n", "
12 | \n", "Numeric imputation | \n", "mean | \n", "
13 | \n", "Categorical imputation | \n", "mode | \n", "
14 | \n", "Maximum one-hot encoding | \n", "25 | \n", "
15 | \n", "Encoding method | \n", "None | \n", "
16 | \n", "Fold Generator | \n", "KFold | \n", "
17 | \n", "Fold Number | \n", "10 | \n", "
18 | \n", "CPU Jobs | \n", "-1 | \n", "
19 | \n", "Use GPU | \n", "False | \n", "
20 | \n", "Log Experiment | \n", "False | \n", "
21 | \n", "Experiment Name | \n", "reg-default-name | \n", "
22 | \n", "USI | \n", "063d | \n", "
\n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|
gbr | \n", "Gradient Boosting Regressor | \n", "2701.9919 | \n", "23548657.1177 | \n", "4832.9329 | \n", "0.8320 | \n", "0.4447 | \n", "0.3137 | \n", "0.0570 | \n", "
rf | \n", "Random Forest Regressor | \n", "2771.4583 | \n", "25416502.3827 | \n", "5028.6343 | \n", "0.8172 | \n", "0.4690 | \n", "0.3303 | \n", "0.0690 | \n", "
catboost | \n", "CatBoost Regressor | \n", "2899.3783 | \n", "25762701.9552 | \n", "5057.5721 | \n", "0.8163 | \n", "0.4815 | \n", "0.3522 | \n", "0.0800 | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "2992.1828 | \n", "25521038.3331 | \n", "5042.0978 | \n", "0.8149 | \n", "0.5378 | \n", "0.3751 | \n", "0.1890 | \n", "
et | \n", "Extra Trees Regressor | \n", "2833.3624 | \n", "28427844.2412 | \n", "5305.6516 | \n", "0.7991 | \n", "0.4877 | \n", "0.3363 | \n", "0.0710 | \n", "
ada | \n", "AdaBoost Regressor | \n", "4316.0568 | \n", "29220505.6498 | \n", "5398.4561 | \n", "0.7903 | \n", "0.6368 | \n", "0.7394 | \n", "0.0420 | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "3443.6091 | \n", "32824626.4000 | \n", "5711.2140 | \n", "0.7626 | \n", "0.6224 | \n", "0.4469 | \n", "0.0420 | \n", "
llar | \n", "Lasso Least Angle Regression | \n", "4298.6038 | \n", "38369142.0849 | \n", "6174.9424 | \n", "0.7309 | \n", "0.5786 | \n", "0.4424 | \n", "0.0400 | \n", "
ridge | \n", "Ridge Regression | \n", "4317.6984 | \n", "38396435.9578 | \n", "6177.2329 | \n", "0.7306 | \n", "0.5891 | \n", "0.4459 | \n", "0.0380 | \n", "
br | \n", "Bayesian Ridge | \n", "4311.2349 | \n", "38391950.0874 | \n", "6176.8896 | \n", "0.7306 | \n", "0.5910 | \n", "0.4447 | \n", "0.0400 | \n", "
lar | \n", "Least Angle Regression | \n", "4303.5559 | \n", "38388058.4578 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "0.0340 | \n", "
lasso | \n", "Lasso Regression | \n", "4303.7697 | \n", "38386797.6709 | \n", "6176.4824 | \n", "0.7306 | \n", "0.5952 | \n", "0.4434 | \n", "0.0340 | \n", "
lr | \n", "Linear Regression | \n", "4303.5559 | \n", "38388058.4578 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "0.8830 | \n", "
huber | \n", "Huber Regressor | \n", "3463.2216 | \n", "48801106.4612 | \n", "6963.9984 | \n", "0.6544 | \n", "0.4927 | \n", "0.2212 | \n", "0.0440 | \n", "
dt | \n", "Decision Tree Regressor | \n", "3383.4916 | \n", "47823199.0729 | \n", "6895.7016 | \n", "0.6497 | \n", "0.5602 | \n", "0.4013 | \n", "0.0390 | \n", "
omp | \n", "Orthogonal Matching Pursuit | \n", "5754.7769 | \n", "57503207.7233 | \n", "7566.7086 | \n", "0.5997 | \n", "0.7418 | \n", "0.8990 | \n", "0.0430 | \n", "
par | \n", "Passive Aggressive Regressor | \n", "4537.0122 | \n", "67346309.9218 | \n", "8142.7826 | \n", "0.5422 | \n", "0.5276 | \n", "0.3207 | \n", "0.0420 | \n", "
en | \n", "Elastic Net | \n", "7372.5238 | \n", "90450782.5713 | \n", "9468.3193 | \n", "0.3792 | \n", "0.7342 | \n", "0.9184 | \n", "0.0390 | \n", "
knn | \n", "K Neighbors Regressor | \n", "8007.7997 | \n", "131387268.8000 | \n", "11425.3695 | \n", "0.0859 | \n", "0.8535 | \n", "0.9232 | \n", "0.0430 | \n", "
dummy | \n", "Dummy Regressor | \n", "9192.5418 | \n", "148516792.8000 | \n", "12132.4733 | \n", "-0.0175 | \n", "1.0154 | \n", "1.5637 | \n", "0.0410 | \n", "
\n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|
gbr | \n", "Gradient Boosting Regressor | \n", "2701.9919 | \n", "23548657.1177 | \n", "4832.9329 | \n", "0.8320 | \n", "0.4447 | \n", "0.3137 | \n", "0.0540 | \n", "
rf | \n", "Random Forest Regressor | \n", "2771.4583 | \n", "25416502.3827 | \n", "5028.6343 | \n", "0.8172 | \n", "0.4690 | \n", "0.3303 | \n", "0.0710 | \n", "
catboost | \n", "CatBoost Regressor | \n", "2899.3783 | \n", "25762701.9552 | \n", "5057.5721 | \n", "0.8163 | \n", "0.4815 | \n", "0.3522 | \n", "0.0370 | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "2992.1828 | \n", "25521038.3331 | \n", "5042.0978 | \n", "0.8149 | \n", "0.5378 | \n", "0.3751 | \n", "0.0470 | \n", "
et | \n", "Extra Trees Regressor | \n", "2833.3624 | \n", "28427844.2412 | \n", "5305.6516 | \n", "0.7991 | \n", "0.4877 | \n", "0.3363 | \n", "0.0730 | \n", "
ada | \n", "AdaBoost Regressor | \n", "4316.0568 | \n", "29220505.6498 | \n", "5398.4561 | \n", "0.7903 | \n", "0.6368 | \n", "0.7394 | \n", "0.0430 | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "3443.6091 | \n", "32824626.4000 | \n", "5711.2140 | \n", "0.7626 | \n", "0.6224 | \n", "0.4469 | \n", "0.0390 | \n", "
llar | \n", "Lasso Least Angle Regression | \n", "4298.6038 | \n", "38369142.0849 | \n", "6174.9424 | \n", "0.7309 | \n", "0.5786 | \n", "0.4424 | \n", "0.0460 | \n", "
ridge | \n", "Ridge Regression | \n", "4317.6984 | \n", "38396435.9578 | \n", "6177.2329 | \n", "0.7306 | \n", "0.5891 | \n", "0.4459 | \n", "0.0400 | \n", "
br | \n", "Bayesian Ridge | \n", "4311.2349 | \n", "38391950.0874 | \n", "6176.8896 | \n", "0.7306 | \n", "0.5910 | \n", "0.4447 | \n", "0.0400 | \n", "
lar | \n", "Least Angle Regression | \n", "4303.5559 | \n", "38388058.4578 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "0.0360 | \n", "
lasso | \n", "Lasso Regression | \n", "4303.7697 | \n", "38386797.6709 | \n", "6176.4824 | \n", "0.7306 | \n", "0.5952 | \n", "0.4434 | \n", "0.0360 | \n", "
lr | \n", "Linear Regression | \n", "4303.5559 | \n", "38388058.4578 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "0.0420 | \n", "
huber | \n", "Huber Regressor | \n", "3463.2216 | \n", "48801106.4612 | \n", "6963.9984 | \n", "0.6544 | \n", "0.4927 | \n", "0.2212 | \n", "0.0460 | \n", "
dt | \n", "Decision Tree Regressor | \n", "3383.4916 | \n", "47823199.0729 | \n", "6895.7016 | \n", "0.6497 | \n", "0.5602 | \n", "0.4013 | \n", "0.0390 | \n", "
omp | \n", "Orthogonal Matching Pursuit | \n", "5754.7769 | \n", "57503207.7233 | \n", "7566.7086 | \n", "0.5997 | \n", "0.7418 | \n", "0.8990 | \n", "0.0420 | \n", "
par | \n", "Passive Aggressive Regressor | \n", "4537.0122 | \n", "67346309.9218 | \n", "8142.7826 | \n", "0.5422 | \n", "0.5276 | \n", "0.3207 | \n", "0.0440 | \n", "
en | \n", "Elastic Net | \n", "7372.5238 | \n", "90450782.5713 | \n", "9468.3193 | \n", "0.3792 | \n", "0.7342 | \n", "0.9184 | \n", "0.0460 | \n", "
knn | \n", "K Neighbors Regressor | \n", "8007.7997 | \n", "131387268.8000 | \n", "11425.3695 | \n", "0.0859 | \n", "0.8535 | \n", "0.9232 | \n", "0.0430 | \n", "
dummy | \n", "Dummy Regressor | \n", "9192.5418 | \n", "148516792.8000 | \n", "12132.4733 | \n", "-0.0175 | \n", "1.0154 | \n", "1.5637 | \n", "0.0440 | \n", "
GradientBoostingRegressor(random_state=123)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GradientBoostingRegressor(random_state=123)
\n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|---|
0 | \n", "Gradient Boosting Regressor | \n", "2392.5661 | \n", "17148355.3169 | \n", "4141.0573 | \n", "0.8800 | \n", "0.3928 | \n", "0.2875 | \n", "
\n", " | age | \n", "sex | \n", "bmi | \n", "children | \n", "smoker | \n", "region | \n", "charges | \n", "prediction_label | \n", "
---|---|---|---|---|---|---|---|---|
936 | \n", "49 | \n", "female | \n", "42.680000 | \n", "2 | \n", "no | \n", "southeast | \n", "9800.888672 | \n", "10681.513104 | \n", "
937 | \n", "32 | \n", "male | \n", "37.334999 | \n", "1 | \n", "no | \n", "northeast | \n", "4667.607422 | \n", "8043.453463 | \n", "
938 | \n", "27 | \n", "female | \n", "31.400000 | \n", "0 | \n", "yes | \n", "southwest | \n", "34838.871094 | \n", "36153.097686 | \n", "
939 | \n", "35 | \n", "male | \n", "24.129999 | \n", "1 | \n", "no | \n", "northwest | \n", "5125.215820 | \n", "7435.516853 | \n", "
940 | \n", "60 | \n", "male | \n", "25.740000 | \n", "0 | \n", "no | \n", "southeast | \n", "12142.578125 | \n", "14676.544334 | \n", "
\n", " | age | \n", "sex | \n", "bmi | \n", "children | \n", "smoker | \n", "region | \n", "
---|---|---|---|---|---|---|
0 | \n", "19 | \n", "female | \n", "27.900 | \n", "0 | \n", "yes | \n", "southwest | \n", "
1 | \n", "18 | \n", "male | \n", "33.770 | \n", "1 | \n", "no | \n", "southeast | \n", "
2 | \n", "28 | \n", "male | \n", "33.000 | \n", "3 | \n", "no | \n", "southeast | \n", "
3 | \n", "33 | \n", "male | \n", "22.705 | \n", "0 | \n", "no | \n", "northwest | \n", "
4 | \n", "32 | \n", "male | \n", "28.880 | \n", "0 | \n", "no | \n", "northwest | \n", "
\n", " | age | \n", "sex | \n", "bmi | \n", "children | \n", "smoker | \n", "region | \n", "prediction_label | \n", "
---|---|---|---|---|---|---|---|
0 | \n", "19 | \n", "female | \n", "27.900000 | \n", "0 | \n", "yes | \n", "southwest | \n", "18464.334448 | \n", "
1 | \n", "18 | \n", "male | \n", "33.770000 | \n", "1 | \n", "no | \n", "southeast | \n", "4020.345384 | \n", "
2 | \n", "28 | \n", "male | \n", "33.000000 | \n", "3 | \n", "no | \n", "southeast | \n", "6555.388388 | \n", "
3 | \n", "33 | \n", "male | \n", "22.705000 | \n", "0 | \n", "no | \n", "northwest | \n", "9627.045725 | \n", "
4 | \n", "32 | \n", "male | \n", "28.879999 | \n", "0 | \n", "no | \n", "northwest | \n", "3325.531292 | \n", "
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('trained_model', GradientBoostingRegressor(random_state=123))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('trained_model', GradientBoostingRegressor(random_state=123))])
TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))
SimpleImputer(strategy='most_frequent')
SimpleImputer(strategy='most_frequent')
TransformerWrapper(include=['sex', 'smoker'],\n", " transformer=OrdinalEncoder(cols=['sex', 'smoker'],\n", " handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
GradientBoostingRegressor(random_state=123)
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "Session id | \n", "123 | \n", "
1 | \n", "Target | \n", "charges | \n", "
2 | \n", "Target type | \n", "Regression | \n", "
3 | \n", "Original data shape | \n", "(1338, 7) | \n", "
4 | \n", "Transformed data shape | \n", "(1338, 10) | \n", "
5 | \n", "Transformed train set shape | \n", "(936, 10) | \n", "
6 | \n", "Transformed test set shape | \n", "(402, 10) | \n", "
7 | \n", "Ordinal features | \n", "2 | \n", "
8 | \n", "Numeric features | \n", "3 | \n", "
9 | \n", "Categorical features | \n", "3 | \n", "
10 | \n", "Preprocess | \n", "True | \n", "
11 | \n", "Imputation type | \n", "simple | \n", "
12 | \n", "Numeric imputation | \n", "mean | \n", "
13 | \n", "Categorical imputation | \n", "mode | \n", "
14 | \n", "Maximum one-hot encoding | \n", "25 | \n", "
15 | \n", "Encoding method | \n", "None | \n", "
16 | \n", "Fold Generator | \n", "KFold | \n", "
17 | \n", "Fold Number | \n", "10 | \n", "
18 | \n", "CPU Jobs | \n", "-1 | \n", "
19 | \n", "Use GPU | \n", "False | \n", "
20 | \n", "Log Experiment | \n", "False | \n", "
21 | \n", "Experiment Name | \n", "reg-default-name | \n", "
22 | \n", "USI | \n", "02ce | \n", "
\n", " | age | \n", "sex | \n", "bmi | \n", "children | \n", "smoker | \n", "region_northeast | \n", "region_southwest | \n", "region_southeast | \n", "region_northwest | \n", "
---|---|---|---|---|---|---|---|---|---|
0 | \n", "36.0 | \n", "1.0 | \n", "27.549999 | \n", "3.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
1 | \n", "60.0 | \n", "0.0 | \n", "35.099998 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "
2 | \n", "30.0 | \n", "1.0 | \n", "31.570000 | \n", "3.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "
3 | \n", "49.0 | \n", "1.0 | \n", "25.600000 | \n", "2.0 | \n", "1.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "
4 | \n", "26.0 | \n", "1.0 | \n", "32.900002 | \n", "2.0 | \n", "1.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
931 | \n", "37.0 | \n", "1.0 | \n", "22.705000 | \n", "3.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
932 | \n", "20.0 | \n", "0.0 | \n", "31.920000 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "
933 | \n", "19.0 | \n", "0.0 | \n", "28.400000 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "
934 | \n", "18.0 | \n", "1.0 | \n", "23.084999 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
935 | \n", "53.0 | \n", "0.0 | \n", "36.860001 | \n", "3.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "
936 rows × 9 columns
\n", "\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "Session id | \n", "123 | \n", "
1 | \n", "Target | \n", "charges | \n", "
2 | \n", "Target type | \n", "Regression | \n", "
3 | \n", "Original data shape | \n", "(1338, 7) | \n", "
4 | \n", "Transformed data shape | \n", "(1338, 10) | \n", "
5 | \n", "Transformed train set shape | \n", "(936, 10) | \n", "
6 | \n", "Transformed test set shape | \n", "(402, 10) | \n", "
7 | \n", "Ordinal features | \n", "2 | \n", "
8 | \n", "Numeric features | \n", "3 | \n", "
9 | \n", "Categorical features | \n", "3 | \n", "
10 | \n", "Preprocess | \n", "True | \n", "
11 | \n", "Imputation type | \n", "simple | \n", "
12 | \n", "Numeric imputation | \n", "mean | \n", "
13 | \n", "Categorical imputation | \n", "mode | \n", "
14 | \n", "Maximum one-hot encoding | \n", "25 | \n", "
15 | \n", "Encoding method | \n", "None | \n", "
16 | \n", "Normalize | \n", "True | \n", "
17 | \n", "Normalize method | \n", "minmax | \n", "
18 | \n", "Fold Generator | \n", "KFold | \n", "
19 | \n", "Fold Number | \n", "10 | \n", "
20 | \n", "CPU Jobs | \n", "-1 | \n", "
21 | \n", "Use GPU | \n", "False | \n", "
22 | \n", "Log Experiment | \n", "False | \n", "
23 | \n", "Experiment Name | \n", "reg-default-name | \n", "
24 | \n", "USI | \n", "3dce | \n", "
\n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|
gbr | \n", "Gradient Boosting Regressor | \n", "2701.9135 | \n", "23548622.1598 | \n", "4832.9291 | \n", "0.8320 | \n", "0.4447 | \n", "0.3137 | \n", "0.0620 | \n", "
rf | \n", "Random Forest Regressor | \n", "2772.9195 | \n", "25409792.9692 | \n", "5028.1973 | \n", "0.8173 | \n", "0.4687 | \n", "0.3298 | \n", "0.0750 | \n", "
catboost | \n", "CatBoost Regressor | \n", "2899.4825 | \n", "25762752.2096 | \n", "5057.5778 | \n", "0.8163 | \n", "0.4815 | \n", "0.3522 | \n", "0.0430 | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "3001.8884 | \n", "25547324.5813 | \n", "5044.5767 | \n", "0.8147 | \n", "0.5445 | \n", "0.3784 | \n", "0.0520 | \n", "
et | \n", "Extra Trees Regressor | \n", "2833.3624 | \n", "28427844.2412 | \n", "5305.6516 | \n", "0.7991 | \n", "0.4877 | \n", "0.3363 | \n", "0.0800 | \n", "
ada | \n", "AdaBoost Regressor | \n", "4175.5916 | \n", "28401799.0579 | \n", "5321.7006 | \n", "0.7976 | \n", "0.6263 | \n", "0.7144 | \n", "0.0490 | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "3439.8892 | \n", "32826514.4000 | \n", "5711.7335 | \n", "0.7626 | \n", "0.6221 | \n", "0.4465 | \n", "0.0450 | \n", "
llar | \n", "Lasso Least Angle Regression | \n", "4298.6038 | \n", "38369142.0849 | \n", "6174.9424 | \n", "0.7309 | \n", "0.5786 | \n", "0.4424 | \n", "0.0360 | \n", "
ridge | \n", "Ridge Regression | \n", "4296.0642 | \n", "38392999.7849 | \n", "6176.6160 | \n", "0.7308 | \n", "0.5710 | \n", "0.4397 | \n", "0.0390 | \n", "
br | \n", "Bayesian Ridge | \n", "4300.6286 | \n", "38387539.9069 | \n", "6176.4192 | \n", "0.7307 | \n", "0.5881 | \n", "0.4419 | \n", "0.0500 | \n", "
lasso | \n", "Lasso Regression | \n", "4302.2469 | \n", "38386534.5553 | \n", "6176.4463 | \n", "0.7306 | \n", "0.5913 | \n", "0.4430 | \n", "0.0410 | \n", "
lar | \n", "Least Angle Regression | \n", "4303.5559 | \n", "38388058.4578 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "0.0390 | \n", "
lr | \n", "Linear Regression | \n", "4312.6186 | \n", "38452749.8007 | \n", "6182.4796 | \n", "0.7298 | \n", "0.6285 | \n", "0.4460 | \n", "0.0380 | \n", "
knn | \n", "K Neighbors Regressor | \n", "3778.4582 | \n", "38143971.2000 | \n", "6165.0463 | \n", "0.7277 | \n", "0.5027 | \n", "0.3690 | \n", "0.0400 | \n", "
par | \n", "Passive Aggressive Regressor | \n", "3536.1733 | \n", "48501878.1363 | \n", "6940.1967 | \n", "0.6566 | \n", "0.4785 | \n", "0.2154 | \n", "0.0430 | \n", "
huber | \n", "Huber Regressor | \n", "3461.7327 | \n", "49057640.5613 | \n", "6981.8576 | \n", "0.6528 | \n", "0.4815 | \n", "0.2188 | \n", "0.0450 | \n", "
dt | \n", "Decision Tree Regressor | \n", "3399.1402 | \n", "48100203.3847 | \n", "6915.2984 | \n", "0.6476 | \n", "0.5629 | \n", "0.4052 | \n", "0.0410 | \n", "
omp | \n", "Orthogonal Matching Pursuit | \n", "5754.7769 | \n", "57503207.7233 | \n", "7566.7086 | \n", "0.5997 | \n", "0.7418 | \n", "0.8990 | \n", "0.0440 | \n", "
en | \n", "Elastic Net | \n", "7571.4598 | \n", "104738034.4707 | \n", "10182.3291 | \n", "0.2846 | \n", "0.8954 | \n", "1.2888 | \n", "0.0380 | \n", "
dummy | \n", "Dummy Regressor | \n", "9192.5418 | \n", "148516792.8000 | \n", "12132.4733 | \n", "-0.0175 | \n", "1.0154 | \n", "1.5637 | \n", "0.0420 | \n", "
\n", " | Name | \n", "Reference | \n", "Turbo | \n", "
---|---|---|---|
ID | \n", "\n", " | \n", " | \n", " |
lr | \n", "Linear Regression | \n", "sklearn.linear_model._base.LinearRegression | \n", "True | \n", "
lasso | \n", "Lasso Regression | \n", "sklearn.linear_model._coordinate_descent.Lasso | \n", "True | \n", "
ridge | \n", "Ridge Regression | \n", "sklearn.linear_model._ridge.Ridge | \n", "True | \n", "
en | \n", "Elastic Net | \n", "sklearn.linear_model._coordinate_descent.Elast... | \n", "True | \n", "
lar | \n", "Least Angle Regression | \n", "sklearn.linear_model._least_angle.Lars | \n", "True | \n", "
llar | \n", "Lasso Least Angle Regression | \n", "sklearn.linear_model._least_angle.LassoLars | \n", "True | \n", "
omp | \n", "Orthogonal Matching Pursuit | \n", "sklearn.linear_model._omp.OrthogonalMatchingPu... | \n", "True | \n", "
br | \n", "Bayesian Ridge | \n", "sklearn.linear_model._bayes.BayesianRidge | \n", "True | \n", "
ard | \n", "Automatic Relevance Determination | \n", "sklearn.linear_model._bayes.ARDRegression | \n", "False | \n", "
par | \n", "Passive Aggressive Regressor | \n", "sklearn.linear_model._passive_aggressive.Passi... | \n", "True | \n", "
ransac | \n", "Random Sample Consensus | \n", "sklearn.linear_model._ransac.RANSACRegressor | \n", "False | \n", "
tr | \n", "TheilSen Regressor | \n", "sklearn.linear_model._theil_sen.TheilSenRegressor | \n", "False | \n", "
huber | \n", "Huber Regressor | \n", "sklearn.linear_model._huber.HuberRegressor | \n", "True | \n", "
kr | \n", "Kernel Ridge | \n", "sklearn.kernel_ridge.KernelRidge | \n", "False | \n", "
svm | \n", "Support Vector Regression | \n", "sklearn.svm._classes.SVR | \n", "False | \n", "
knn | \n", "K Neighbors Regressor | \n", "sklearn.neighbors._regression.KNeighborsRegressor | \n", "True | \n", "
dt | \n", "Decision Tree Regressor | \n", "sklearn.tree._classes.DecisionTreeRegressor | \n", "True | \n", "
rf | \n", "Random Forest Regressor | \n", "sklearn.ensemble._forest.RandomForestRegressor | \n", "True | \n", "
et | \n", "Extra Trees Regressor | \n", "sklearn.ensemble._forest.ExtraTreesRegressor | \n", "True | \n", "
ada | \n", "AdaBoost Regressor | \n", "sklearn.ensemble._weight_boosting.AdaBoostRegr... | \n", "True | \n", "
gbr | \n", "Gradient Boosting Regressor | \n", "sklearn.ensemble._gb.GradientBoostingRegressor | \n", "True | \n", "
mlp | \n", "MLP Regressor | \n", "sklearn.neural_network._multilayer_perceptron.... | \n", "False | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "xgboost.sklearn.XGBRegressor | \n", "True | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "lightgbm.sklearn.LGBMRegressor | \n", "True | \n", "
catboost | \n", "CatBoost Regressor | \n", "catboost.core.CatBoostRegressor | \n", "True | \n", "
dummy | \n", "Dummy Regressor | \n", "sklearn.dummy.DummyRegressor | \n", "True | \n", "
\n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|
gbr | \n", "Gradient Boosting Regressor | \n", "2701.9135 | \n", "23548622.1598 | \n", "4832.9291 | \n", "0.8320 | \n", "0.4447 | \n", "0.3137 | \n", "0.0640 | \n", "
rf | \n", "Random Forest Regressor | \n", "2772.9195 | \n", "25409792.9692 | \n", "5028.1973 | \n", "0.8173 | \n", "0.4687 | \n", "0.3298 | \n", "0.0750 | \n", "
catboost | \n", "CatBoost Regressor | \n", "2899.4825 | \n", "25762752.2096 | \n", "5057.5778 | \n", "0.8163 | \n", "0.4815 | \n", "0.3522 | \n", "0.0460 | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "3001.8884 | \n", "25547324.5813 | \n", "5044.5767 | \n", "0.8147 | \n", "0.5445 | \n", "0.3784 | \n", "0.0480 | \n", "
et | \n", "Extra Trees Regressor | \n", "2833.3624 | \n", "28427844.2412 | \n", "5305.6516 | \n", "0.7991 | \n", "0.4877 | \n", "0.3363 | \n", "0.0760 | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "3439.8892 | \n", "32826514.4000 | \n", "5711.7335 | \n", "0.7626 | \n", "0.6221 | \n", "0.4465 | \n", "0.0420 | \n", "
dt | \n", "Decision Tree Regressor | \n", "3399.1402 | \n", "48100203.3847 | \n", "6915.2984 | \n", "0.6476 | \n", "0.5629 | \n", "0.4052 | \n", "0.0410 | \n", "
GradientBoostingRegressor(random_state=123)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GradientBoostingRegressor(random_state=123)
\n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|
gbr | \n", "Gradient Boosting Regressor | \n", "2701.9135 | \n", "2.354862e+07 | \n", "4832.9291 | \n", "0.8320 | \n", "0.4447 | \n", "0.3137 | \n", "0.064 | \n", "
rf | \n", "Random Forest Regressor | \n", "2772.9195 | \n", "2.540979e+07 | \n", "5028.1973 | \n", "0.8173 | \n", "0.4687 | \n", "0.3298 | \n", "0.075 | \n", "
catboost | \n", "CatBoost Regressor | \n", "2899.4825 | \n", "2.576275e+07 | \n", "5057.5778 | \n", "0.8163 | \n", "0.4815 | \n", "0.3522 | \n", "0.046 | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "3001.8884 | \n", "2.554732e+07 | \n", "5044.5767 | \n", "0.8147 | \n", "0.5445 | \n", "0.3784 | \n", "0.048 | \n", "
et | \n", "Extra Trees Regressor | \n", "2833.3624 | \n", "2.842784e+07 | \n", "5305.6516 | \n", "0.7991 | \n", "0.4877 | \n", "0.3363 | \n", "0.076 | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "3439.8892 | \n", "3.282651e+07 | \n", "5711.7335 | \n", "0.7626 | \n", "0.6221 | \n", "0.4465 | \n", "0.042 | \n", "
dt | \n", "Decision Tree Regressor | \n", "3399.1402 | \n", "4.810020e+07 | \n", "6915.2984 | \n", "0.6476 | \n", "0.5629 | \n", "0.4052 | \n", "0.041 | \n", "
\n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|
gbr | \n", "Gradient Boosting Regressor | \n", "2701.9135 | \n", "23548622.1598 | \n", "4832.9291 | \n", "0.8320 | \n", "0.4447 | \n", "0.3137 | \n", "0.0640 | \n", "
rf | \n", "Random Forest Regressor | \n", "2772.9195 | \n", "25409792.9692 | \n", "5028.1973 | \n", "0.8173 | \n", "0.4687 | \n", "0.3298 | \n", "0.0800 | \n", "
et | \n", "Extra Trees Regressor | \n", "2833.3624 | \n", "28427844.2412 | \n", "5305.6516 | \n", "0.7991 | \n", "0.4877 | \n", "0.3363 | \n", "0.0800 | \n", "
catboost | \n", "CatBoost Regressor | \n", "2899.4825 | \n", "25762752.2096 | \n", "5057.5778 | \n", "0.8163 | \n", "0.4815 | \n", "0.3522 | \n", "0.0420 | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "3001.8884 | \n", "25547324.5813 | \n", "5044.5767 | \n", "0.8147 | \n", "0.5445 | \n", "0.3784 | \n", "0.0500 | \n", "
dt | \n", "Decision Tree Regressor | \n", "3399.1402 | \n", "48100203.3847 | \n", "6915.2984 | \n", "0.6476 | \n", "0.5629 | \n", "0.4052 | \n", "0.0430 | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "3439.8892 | \n", "32826514.4000 | \n", "5711.7335 | \n", "0.7626 | \n", "0.6221 | \n", "0.4465 | \n", "0.0530 | \n", "
huber | \n", "Huber Regressor | \n", "3461.7327 | \n", "49057640.5613 | \n", "6981.8576 | \n", "0.6528 | \n", "0.4815 | \n", "0.2188 | \n", "0.0490 | \n", "
par | \n", "Passive Aggressive Regressor | \n", "3536.1733 | \n", "48501878.1363 | \n", "6940.1967 | \n", "0.6566 | \n", "0.4785 | \n", "0.2154 | \n", "0.0480 | \n", "
knn | \n", "K Neighbors Regressor | \n", "3778.4582 | \n", "38143971.2000 | \n", "6165.0463 | \n", "0.7277 | \n", "0.5027 | \n", "0.3690 | \n", "0.0470 | \n", "
ada | \n", "AdaBoost Regressor | \n", "4175.5916 | \n", "28401799.0579 | \n", "5321.7006 | \n", "0.7976 | \n", "0.6263 | \n", "0.7144 | \n", "0.0470 | \n", "
ridge | \n", "Ridge Regression | \n", "4296.0642 | \n", "38392999.7849 | \n", "6176.6160 | \n", "0.7308 | \n", "0.5710 | \n", "0.4397 | \n", "0.0420 | \n", "
llar | \n", "Lasso Least Angle Regression | \n", "4298.6038 | \n", "38369142.0849 | \n", "6174.9424 | \n", "0.7309 | \n", "0.5786 | \n", "0.4424 | \n", "0.0450 | \n", "
br | \n", "Bayesian Ridge | \n", "4300.6286 | \n", "38387539.9069 | \n", "6176.4192 | \n", "0.7307 | \n", "0.5881 | \n", "0.4419 | \n", "0.0480 | \n", "
lasso | \n", "Lasso Regression | \n", "4302.2469 | \n", "38386534.5553 | \n", "6176.4463 | \n", "0.7306 | \n", "0.5913 | \n", "0.4430 | \n", "0.0430 | \n", "
lar | \n", "Least Angle Regression | \n", "4303.5559 | \n", "38388058.4578 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "0.0420 | \n", "
lr | \n", "Linear Regression | \n", "4312.6186 | \n", "38452749.8007 | \n", "6182.4796 | \n", "0.7298 | \n", "0.6285 | \n", "0.4460 | \n", "0.0430 | \n", "
omp | \n", "Orthogonal Matching Pursuit | \n", "5754.7769 | \n", "57503207.7233 | \n", "7566.7086 | \n", "0.5997 | \n", "0.7418 | \n", "0.8990 | \n", "0.0460 | \n", "
en | \n", "Elastic Net | \n", "7571.4598 | \n", "104738034.4707 | \n", "10182.3291 | \n", "0.2846 | \n", "0.8954 | \n", "1.2888 | \n", "0.0450 | \n", "
dummy | \n", "Dummy Regressor | \n", "9192.5418 | \n", "148516792.8000 | \n", "12132.4733 | \n", "-0.0175 | \n", "1.0154 | \n", "1.5637 | \n", "0.0400 | \n", "
\n", " | Name | \n", "Reference | \n", "Turbo | \n", "
---|---|---|---|
ID | \n", "\n", " | \n", " | \n", " |
lr | \n", "Linear Regression | \n", "sklearn.linear_model._base.LinearRegression | \n", "True | \n", "
lasso | \n", "Lasso Regression | \n", "sklearn.linear_model._coordinate_descent.Lasso | \n", "True | \n", "
ridge | \n", "Ridge Regression | \n", "sklearn.linear_model._ridge.Ridge | \n", "True | \n", "
en | \n", "Elastic Net | \n", "sklearn.linear_model._coordinate_descent.Elast... | \n", "True | \n", "
lar | \n", "Least Angle Regression | \n", "sklearn.linear_model._least_angle.Lars | \n", "True | \n", "
llar | \n", "Lasso Least Angle Regression | \n", "sklearn.linear_model._least_angle.LassoLars | \n", "True | \n", "
omp | \n", "Orthogonal Matching Pursuit | \n", "sklearn.linear_model._omp.OrthogonalMatchingPu... | \n", "True | \n", "
br | \n", "Bayesian Ridge | \n", "sklearn.linear_model._bayes.BayesianRidge | \n", "True | \n", "
ard | \n", "Automatic Relevance Determination | \n", "sklearn.linear_model._bayes.ARDRegression | \n", "False | \n", "
par | \n", "Passive Aggressive Regressor | \n", "sklearn.linear_model._passive_aggressive.Passi... | \n", "True | \n", "
ransac | \n", "Random Sample Consensus | \n", "sklearn.linear_model._ransac.RANSACRegressor | \n", "False | \n", "
tr | \n", "TheilSen Regressor | \n", "sklearn.linear_model._theil_sen.TheilSenRegressor | \n", "False | \n", "
huber | \n", "Huber Regressor | \n", "sklearn.linear_model._huber.HuberRegressor | \n", "True | \n", "
kr | \n", "Kernel Ridge | \n", "sklearn.kernel_ridge.KernelRidge | \n", "False | \n", "
svm | \n", "Support Vector Regression | \n", "sklearn.svm._classes.SVR | \n", "False | \n", "
knn | \n", "K Neighbors Regressor | \n", "sklearn.neighbors._regression.KNeighborsRegressor | \n", "True | \n", "
dt | \n", "Decision Tree Regressor | \n", "sklearn.tree._classes.DecisionTreeRegressor | \n", "True | \n", "
rf | \n", "Random Forest Regressor | \n", "sklearn.ensemble._forest.RandomForestRegressor | \n", "True | \n", "
et | \n", "Extra Trees Regressor | \n", "sklearn.ensemble._forest.ExtraTreesRegressor | \n", "True | \n", "
ada | \n", "AdaBoost Regressor | \n", "sklearn.ensemble._weight_boosting.AdaBoostRegr... | \n", "True | \n", "
gbr | \n", "Gradient Boosting Regressor | \n", "sklearn.ensemble._gb.GradientBoostingRegressor | \n", "True | \n", "
mlp | \n", "MLP Regressor | \n", "sklearn.neural_network._multilayer_perceptron.... | \n", "False | \n", "
xgboost | \n", "Extreme Gradient Boosting | \n", "xgboost.sklearn.XGBRegressor | \n", "True | \n", "
lightgbm | \n", "Light Gradient Boosting Machine | \n", "lightgbm.sklearn.LGBMRegressor | \n", "True | \n", "
catboost | \n", "CatBoost Regressor | \n", "catboost.core.CatBoostRegressor | \n", "True | \n", "
dummy | \n", "Dummy Regressor | \n", "sklearn.dummy.DummyRegressor | \n", "True | \n", "
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "4221.7662 | \n", "33767244.1606 | \n", "5810.9590 | \n", "0.7983 | \n", "0.4864 | \n", "0.4323 | \n", "
1 | \n", "4529.8902 | \n", "43625181.5268 | \n", "6604.9361 | \n", "0.7463 | \n", "0.5543 | \n", "0.4301 | \n", "
2 | \n", "3958.4660 | \n", "32631291.9087 | \n", "5712.3806 | \n", "0.5868 | \n", "1.0240 | \n", "0.4630 | \n", "
3 | \n", "3725.8887 | \n", "26679679.2570 | \n", "5165.2376 | \n", "0.7772 | \n", "0.4979 | \n", "0.5219 | \n", "
4 | \n", "4437.1204 | \n", "43552381.4341 | \n", "6599.4228 | \n", "0.6761 | \n", "0.5731 | \n", "0.3768 | \n", "
5 | \n", "4115.6340 | \n", "35844995.0079 | \n", "5987.0690 | \n", "0.7694 | \n", "0.5381 | \n", "0.4131 | \n", "
6 | \n", "4098.0868 | \n", "39631320.0598 | \n", "6295.3411 | \n", "0.7303 | \n", "0.5745 | \n", "0.4266 | \n", "
7 | \n", "4850.1058 | \n", "46175035.2997 | \n", "6795.2215 | \n", "0.7461 | \n", "0.5706 | \n", "0.3959 | \n", "
8 | \n", "4621.0616 | \n", "40681916.3737 | \n", "6378.2377 | \n", "0.7372 | \n", "0.7032 | \n", "0.5225 | \n", "
9 | \n", "4568.1661 | \n", "41938452.9786 | \n", "6475.9905 | \n", "0.7299 | \n", "0.7626 | \n", "0.4780 | \n", "
Mean | \n", "4312.6186 | \n", "38452749.8007 | \n", "6182.4796 | \n", "0.7298 | \n", "0.6285 | \n", "0.4460 | \n", "
Std | \n", "327.8412 | \n", "5763256.3224 | \n", "479.2660 | \n", "0.0569 | \n", "0.1550 | \n", "0.0470 | \n", "
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "4221.7662 | \n", "3.376724e+07 | \n", "5810.9590 | \n", "0.7983 | \n", "0.4864 | \n", "0.4323 | \n", "
1 | \n", "4529.8902 | \n", "4.362518e+07 | \n", "6604.9361 | \n", "0.7463 | \n", "0.5543 | \n", "0.4301 | \n", "
2 | \n", "3958.4660 | \n", "3.263129e+07 | \n", "5712.3806 | \n", "0.5868 | \n", "1.0240 | \n", "0.4630 | \n", "
3 | \n", "3725.8887 | \n", "2.667968e+07 | \n", "5165.2376 | \n", "0.7772 | \n", "0.4979 | \n", "0.5219 | \n", "
4 | \n", "4437.1204 | \n", "4.355238e+07 | \n", "6599.4228 | \n", "0.6761 | \n", "0.5731 | \n", "0.3768 | \n", "
5 | \n", "4115.6340 | \n", "3.584500e+07 | \n", "5987.0690 | \n", "0.7694 | \n", "0.5381 | \n", "0.4131 | \n", "
6 | \n", "4098.0868 | \n", "3.963132e+07 | \n", "6295.3411 | \n", "0.7303 | \n", "0.5745 | \n", "0.4266 | \n", "
7 | \n", "4850.1058 | \n", "4.617504e+07 | \n", "6795.2215 | \n", "0.7461 | \n", "0.5706 | \n", "0.3959 | \n", "
8 | \n", "4621.0616 | \n", "4.068192e+07 | \n", "6378.2377 | \n", "0.7372 | \n", "0.7032 | \n", "0.5225 | \n", "
9 | \n", "4568.1661 | \n", "4.193845e+07 | \n", "6475.9905 | \n", "0.7299 | \n", "0.7626 | \n", "0.4780 | \n", "
Mean | \n", "4312.6186 | \n", "3.845275e+07 | \n", "6182.4796 | \n", "0.7298 | \n", "0.6285 | \n", "0.4460 | \n", "
Std | \n", "327.8412 | \n", "5.763256e+06 | \n", "479.2660 | \n", "0.0569 | \n", "0.1550 | \n", "0.0470 | \n", "
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "4170.7537 | \n", "35338831.9346 | \n", "5944.6473 | \n", "0.7482 | \n", "0.6562 | \n", "0.4578 | \n", "
1 | \n", "4285.8970 | \n", "39763353.6903 | \n", "6305.8190 | \n", "0.7176 | \n", "0.5406 | \n", "0.4443 | \n", "
2 | \n", "4511.4189 | \n", "40766553.9170 | \n", "6384.8691 | \n", "0.7492 | \n", "0.6160 | \n", "0.4383 | \n", "
Mean | \n", "4322.6899 | \n", "38622913.1806 | \n", "6211.7785 | \n", "0.7383 | \n", "0.6043 | \n", "0.4468 | \n", "
Std | \n", "141.4885 | \n", "2358035.1845 | \n", "191.6273 | \n", "0.0147 | \n", "0.0479 | \n", "0.0082 | \n", "
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "4222.9616 | \n", "33775764.3432 | \n", "5811.6920 | \n", "0.7983 | \n", "0.4879 | \n", "0.4328 | \n", "
1 | \n", "4522.5819 | \n", "43620030.5192 | \n", "6604.5462 | \n", "0.7464 | \n", "0.5473 | \n", "0.4216 | \n", "
2 | \n", "3853.0378 | \n", "31981107.9602 | \n", "5655.1842 | \n", "0.5951 | \n", "0.7108 | \n", "0.4398 | \n", "
3 | \n", "3707.7705 | \n", "26513348.5760 | \n", "5149.1114 | \n", "0.7786 | \n", "0.4891 | \n", "0.5164 | \n", "
4 | \n", "4484.2122 | \n", "43828444.1000 | \n", "6620.3054 | \n", "0.6740 | \n", "0.5761 | \n", "0.3847 | \n", "
5 | \n", "4113.6222 | \n", "35882341.9810 | \n", "5990.1871 | \n", "0.7692 | \n", "0.5464 | \n", "0.4130 | \n", "
6 | \n", "4098.0868 | \n", "39631320.0598 | \n", "6295.3411 | \n", "0.7303 | \n", "0.5745 | \n", "0.4266 | \n", "
7 | \n", "4833.7747 | \n", "45739275.7172 | \n", "6763.0818 | \n", "0.7485 | \n", "0.5887 | \n", "0.3967 | \n", "
8 | \n", "4621.0616 | \n", "40681916.3737 | \n", "6378.2377 | \n", "0.7372 | \n", "0.7032 | \n", "0.5225 | \n", "
9 | \n", "4578.4499 | \n", "42227034.9476 | \n", "6498.2332 | \n", "0.7280 | \n", "0.7252 | \n", "0.4793 | \n", "
Mean | \n", "4303.5559 | \n", "38388058.4578 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "
Std | \n", "343.6324 | \n", "5849500.5628 | \n", "487.6160 | \n", "0.0553 | \n", "0.0838 | \n", "0.0451 | \n", "
LinearRegression(fit_intercept=False, n_jobs=-1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression(fit_intercept=False, n_jobs=-1)
\n", " | \n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|---|
Split | \n", "Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
CV-Train | \n", "0 | \n", "4267.4267 | \n", "38159913.7820 | \n", "6177.3711 | \n", "0.7384 | \n", "0.5750 | \n", "0.4421 | \n", "
1 | \n", "4234.8525 | \n", "37077265.7216 | \n", "6089.1104 | \n", "0.7449 | \n", "0.7474 | \n", "0.4442 | \n", "|
2 | \n", "4416.2453 | \n", "38462804.3733 | \n", "6201.8388 | \n", "0.7518 | \n", "0.6981 | \n", "0.4727 | \n", "|
3 | \n", "4389.0411 | \n", "38983678.6037 | \n", "6243.6911 | \n", "0.7417 | \n", "0.5697 | \n", "0.4495 | \n", "|
4 | \n", "4212.7173 | \n", "37111422.3767 | \n", "6091.9145 | \n", "0.7520 | \n", "0.6824 | \n", "0.4295 | \n", "|
5 | \n", "4288.5643 | \n", "37909700.1619 | \n", "6157.0854 | \n", "0.7426 | \n", "0.5880 | \n", "0.4443 | \n", "|
6 | \n", "4271.9909 | \n", "37519682.3513 | \n", "6125.3312 | \n", "0.7469 | \n", "0.5497 | \n", "0.4331 | \n", "|
7 | \n", "4164.1720 | \n", "36878048.6417 | \n", "6072.7299 | \n", "0.7440 | \n", "0.5818 | \n", "0.4213 | \n", "|
8 | \n", "4234.1460 | \n", "37404224.4784 | \n", "6115.8993 | \n", "0.7462 | \n", "0.5896 | \n", "0.4291 | \n", "|
9 | \n", "4230.4127 | \n", "37247244.0624 | \n", "6103.0520 | \n", "0.7472 | \n", "0.5590 | \n", "0.4312 | \n", "|
CV-Val | \n", "0 | \n", "4221.7662 | \n", "33767244.1606 | \n", "5810.9590 | \n", "0.7983 | \n", "0.4864 | \n", "0.4323 | \n", "
1 | \n", "4529.8902 | \n", "43625181.5268 | \n", "6604.9361 | \n", "0.7463 | \n", "0.5543 | \n", "0.4301 | \n", "|
2 | \n", "3958.4660 | \n", "32631291.9087 | \n", "5712.3806 | \n", "0.5868 | \n", "1.0240 | \n", "0.4630 | \n", "|
3 | \n", "3725.8887 | \n", "26679679.2570 | \n", "5165.2376 | \n", "0.7772 | \n", "0.4979 | \n", "0.5219 | \n", "|
4 | \n", "4437.1204 | \n", "43552381.4341 | \n", "6599.4228 | \n", "0.6761 | \n", "0.5731 | \n", "0.3768 | \n", "|
5 | \n", "4115.6340 | \n", "35844995.0079 | \n", "5987.0690 | \n", "0.7694 | \n", "0.5381 | \n", "0.4131 | \n", "|
6 | \n", "4098.0868 | \n", "39631320.0598 | \n", "6295.3411 | \n", "0.7303 | \n", "0.5745 | \n", "0.4266 | \n", "|
7 | \n", "4850.1058 | \n", "46175035.2997 | \n", "6795.2215 | \n", "0.7461 | \n", "0.5706 | \n", "0.3959 | \n", "|
8 | \n", "4621.0616 | \n", "40681916.3737 | \n", "6378.2377 | \n", "0.7372 | \n", "0.7032 | \n", "0.5225 | \n", "|
9 | \n", "4568.1661 | \n", "41938452.9786 | \n", "6475.9905 | \n", "0.7299 | \n", "0.7626 | \n", "0.4780 | \n", "|
CV-Train | \n", "Mean | \n", "4270.9569 | \n", "37675398.4553 | \n", "6137.8024 | \n", "0.7456 | \n", "0.6141 | \n", "0.4397 | \n", "
Std | \n", "73.8061 | \n", "649149.8242 | \n", "52.7300 | \n", "0.0040 | \n", "0.0652 | \n", "0.0138 | \n", "|
CV-Val | \n", "Mean | \n", "4312.6186 | \n", "38452749.8007 | \n", "6182.4796 | \n", "0.7298 | \n", "0.6285 | \n", "0.4460 | \n", "
Std | \n", "327.8412 | \n", "5763256.3224 | \n", "479.2660 | \n", "0.0569 | \n", "0.1550 | \n", "0.0470 | \n", "|
Train | \n", "nan | \n", "4200.4677 | \n", "37762351.2375 | \n", "6145.1079 | \n", "0.7451 | \n", "0.6739 | \n", "0.4178 | \n", "
LinearRegression(n_jobs=-1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
LinearRegression(n_jobs=-1)
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "3244.6173 | \n", "45002914.9978 | \n", "6708.4212 | \n", "0.7312 | \n", "0.5884 | \n", "0.4883 | \n", "
1 | \n", "3106.2611 | \n", "45435728.7536 | \n", "6740.6030 | \n", "0.7358 | \n", "0.5389 | \n", "0.3271 | \n", "
2 | \n", "3646.2662 | \n", "54445682.2627 | \n", "7378.7318 | \n", "0.3107 | \n", "0.6475 | \n", "0.4752 | \n", "
3 | \n", "3267.9250 | \n", "45463401.7749 | \n", "6742.6554 | \n", "0.6204 | \n", "0.5751 | \n", "0.4339 | \n", "
4 | \n", "4344.7470 | \n", "65261429.3013 | \n", "8078.4546 | \n", "0.5146 | \n", "0.7261 | \n", "0.6008 | \n", "
5 | \n", "3497.9281 | \n", "42984919.0254 | \n", "6556.2885 | \n", "0.7235 | \n", "0.4614 | \n", "0.3208 | \n", "
6 | \n", "3596.2637 | \n", "53600704.7298 | \n", "7321.2502 | \n", "0.6353 | \n", "0.5284 | \n", "0.4126 | \n", "
7 | \n", "2804.7493 | \n", "37461859.8541 | \n", "6120.6094 | \n", "0.7940 | \n", "0.4737 | \n", "0.1787 | \n", "
8 | \n", "3080.1801 | \n", "42102090.8846 | \n", "6488.6124 | \n", "0.7281 | \n", "0.5168 | \n", "0.4537 | \n", "
9 | \n", "3402.4641 | \n", "49243302.2625 | \n", "7017.3572 | \n", "0.6828 | \n", "0.5725 | \n", "0.3613 | \n", "
Mean | \n", "3399.1402 | \n", "48100203.3847 | \n", "6915.2984 | \n", "0.6476 | \n", "0.5629 | \n", "0.4052 | \n", "
Std | \n", "398.2185 | \n", "7518631.1992 | \n", "528.0642 | \n", "0.1348 | \n", "0.0754 | \n", "0.1094 | \n", "
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "1745.0008 | \n", "18073621.2534 | \n", "4251.3082 | \n", "0.8920 | \n", "0.3408 | \n", "0.1390 | \n", "
1 | \n", "2380.2671 | \n", "33969297.4978 | \n", "5828.3186 | \n", "0.8025 | \n", "0.4803 | \n", "0.1491 | \n", "
2 | \n", "2005.5481 | \n", "23477540.5275 | \n", "4845.3628 | \n", "0.7027 | \n", "0.4742 | \n", "0.1604 | \n", "
3 | \n", "1986.9419 | \n", "22156779.8636 | \n", "4707.0989 | \n", "0.8150 | \n", "0.3731 | \n", "0.1550 | \n", "
4 | \n", "2255.0797 | \n", "28517151.4384 | \n", "5340.1453 | \n", "0.7879 | \n", "0.4832 | \n", "0.1465 | \n", "
5 | \n", "1961.7810 | \n", "20794913.6607 | \n", "4560.1440 | \n", "0.8662 | \n", "0.3653 | \n", "0.1287 | \n", "
6 | \n", "1649.9559 | \n", "20053618.6090 | \n", "4478.1267 | \n", "0.8635 | \n", "0.3315 | \n", "0.1164 | \n", "
7 | \n", "2049.2066 | \n", "26281892.4673 | \n", "5126.5868 | \n", "0.8555 | \n", "0.4653 | \n", "0.1298 | \n", "
8 | \n", "1991.8599 | \n", "23667668.4391 | \n", "4864.9428 | \n", "0.8471 | \n", "0.3865 | \n", "0.1452 | \n", "
9 | \n", "2159.0994 | \n", "26013111.3580 | \n", "5100.3050 | \n", "0.8324 | \n", "0.4242 | \n", "0.1459 | \n", "
Mean | \n", "2018.4740 | \n", "24300559.5115 | \n", "4910.2339 | \n", "0.8265 | \n", "0.4124 | \n", "0.1416 | \n", "
Std | \n", "205.7361 | \n", "4392006.1282 | \n", "436.0762 | \n", "0.0511 | \n", "0.0570 | \n", "0.0126 | \n", "
DecisionTreeRegressor(random_state=123)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeRegressor(random_state=123)
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "2862.1689 | \n", "20651854.5440 | \n", "4544.4312 | \n", "0.8767 | \n", "0.4291 | \n", "0.3378 | \n", "
1 | \n", "2985.5485 | \n", "29278808.6736 | \n", "5410.9896 | \n", "0.8298 | \n", "0.4471 | \n", "0.3012 | \n", "
2 | \n", "2843.3673 | \n", "23854320.1238 | \n", "4884.0885 | \n", "0.6980 | \n", "0.4900 | \n", "0.3620 | \n", "
3 | \n", "2868.1258 | \n", "20204282.7199 | \n", "4494.9174 | \n", "0.8313 | \n", "0.4597 | \n", "0.4100 | \n", "
4 | \n", "3153.2150 | \n", "26237222.1432 | \n", "5122.2282 | \n", "0.8049 | \n", "0.4801 | \n", "0.3419 | \n", "
5 | \n", "2735.1828 | \n", "17885888.8292 | \n", "4229.1712 | \n", "0.8849 | \n", "0.3806 | \n", "0.2917 | \n", "
6 | \n", "2606.7286 | \n", "20086199.5553 | \n", "4481.7630 | \n", "0.8633 | \n", "0.4124 | \n", "0.3367 | \n", "
7 | \n", "2831.0258 | \n", "24114233.9138 | \n", "4910.6246 | \n", "0.8674 | \n", "0.4664 | \n", "0.3333 | \n", "
8 | \n", "2663.4574 | \n", "19629791.0490 | \n", "4430.5520 | \n", "0.8732 | \n", "0.4288 | \n", "0.3656 | \n", "
9 | \n", "2788.2505 | \n", "24885036.6072 | \n", "4988.4904 | \n", "0.8397 | \n", "0.4771 | \n", "0.3403 | \n", "
Mean | \n", "2833.7071 | \n", "22682763.8159 | \n", "4749.7256 | \n", "0.8369 | \n", "0.4471 | \n", "0.3421 | \n", "
Std | \n", "148.1600 | \n", "3372742.1687 | \n", "350.5288 | \n", "0.0522 | \n", "0.0326 | \n", "0.0315 | \n", "
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "1745.0008 | \n", "18073621.2534 | \n", "4251.3082 | \n", "0.8920 | \n", "0.3408 | \n", "0.1390 | \n", "
1 | \n", "2380.2671 | \n", "33969297.4978 | \n", "5828.3186 | \n", "0.8025 | \n", "0.4803 | \n", "0.1491 | \n", "
2 | \n", "2005.5481 | \n", "23477540.5275 | \n", "4845.3628 | \n", "0.7027 | \n", "0.4742 | \n", "0.1604 | \n", "
3 | \n", "1986.9419 | \n", "22156779.8636 | \n", "4707.0989 | \n", "0.8150 | \n", "0.3731 | \n", "0.1550 | \n", "
4 | \n", "2255.0797 | \n", "28517151.4384 | \n", "5340.1453 | \n", "0.7879 | \n", "0.4832 | \n", "0.1465 | \n", "
5 | \n", "1961.7810 | \n", "20794913.6607 | \n", "4560.1440 | \n", "0.8662 | \n", "0.3653 | \n", "0.1287 | \n", "
6 | \n", "1649.9559 | \n", "20053618.6090 | \n", "4478.1267 | \n", "0.8635 | \n", "0.3315 | \n", "0.1164 | \n", "
7 | \n", "2049.2066 | \n", "26281892.4673 | \n", "5126.5868 | \n", "0.8555 | \n", "0.4653 | \n", "0.1298 | \n", "
8 | \n", "1991.8599 | \n", "23667668.4391 | \n", "4864.9428 | \n", "0.8471 | \n", "0.3865 | \n", "0.1452 | \n", "
9 | \n", "2159.0994 | \n", "26013111.3580 | \n", "5100.3050 | \n", "0.8324 | \n", "0.4242 | \n", "0.1459 | \n", "
Mean | \n", "2018.4740 | \n", "24300559.5115 | \n", "4910.2339 | \n", "0.8265 | \n", "0.4124 | \n", "0.1416 | \n", "
Std | \n", "205.7361 | \n", "4392006.1282 | \n", "436.0762 | \n", "0.0511 | \n", "0.0570 | \n", "0.0126 | \n", "
DecisionTreeRegressor(criterion='absolute_error', max_depth=6, max_features=1.0,\n", " min_impurity_decrease=0.002, min_samples_leaf=5,\n", " min_samples_split=5, random_state=123)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeRegressor(criterion='absolute_error', max_depth=6, max_features=1.0,\n", " min_impurity_decrease=0.002, min_samples_leaf=5,\n", " min_samples_split=5, random_state=123)
RandomizedSearchCV(cv=KFold(n_splits=10, random_state=None, shuffle=False),\n", " estimator=Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age',\n", " 'bmi',\n", " 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex',\n", " 'smoker',\n", " 'region'],\n", " tra...\n", " 7, 8, 9,\n", " 10, 11,\n", " 12, 13,\n", " 14, 15,\n", " 16],\n", " 'actual_estimator__max_features': [1.0,\n", " 'sqrt',\n", " 'log2'],\n", " 'actual_estimator__min_impurity_decrease': [0,\n", " 0.0001,\n", " 0.001,\n", " 0.01,\n", " 0.0002,\n", " 0.002,\n", " 0.02,\n", " 0.0005,\n", " 0.005,\n", " 0.05,\n", " 0.1,\n", " 0.2,\n", " 0.3,\n", " 0.4,\n", " 0.5],\n", " 'actual_estimator__min_samples_leaf': [2,\n", " 3,\n", " 4,\n", " 5,\n", " 6],\n", " 'actual_estimator__min_samples_split': [2,\n", " 5,\n", " 7,\n", " 9,\n", " 10]},\n", " random_state=123, refit=False, scoring='r2', verbose=1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
RandomizedSearchCV(cv=KFold(n_splits=10, random_state=None, shuffle=False),\n", " estimator=Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age',\n", " 'bmi',\n", " 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex',\n", " 'smoker',\n", " 'region'],\n", " tra...\n", " 7, 8, 9,\n", " 10, 11,\n", " 12, 13,\n", " 14, 15,\n", " 16],\n", " 'actual_estimator__max_features': [1.0,\n", " 'sqrt',\n", " 'log2'],\n", " 'actual_estimator__min_impurity_decrease': [0,\n", " 0.0001,\n", " 0.001,\n", " 0.01,\n", " 0.0002,\n", " 0.002,\n", " 0.02,\n", " 0.0005,\n", " 0.005,\n", " 0.05,\n", " 0.1,\n", " 0.2,\n", " 0.3,\n", " 0.4,\n", " 0.5],\n", " 'actual_estimator__min_samples_leaf': [2,\n", " 3,\n", " 4,\n", " 5,\n", " 6],\n", " 'actual_estimator__min_samples_split': [2,\n", " 5,\n", " 7,\n", " 9,\n", " 10]},\n", " random_state=123, refit=False, scoring='r2', verbose=1)
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n", " ('actual_estimator', DecisionTreeRegressor(random_state=123))])
TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))
SimpleImputer(strategy='most_frequent')
SimpleImputer(strategy='most_frequent')
TransformerWrapper(include=['sex', 'smoker'],\n", " transformer=OrdinalEncoder(cols=['sex', 'smoker'],\n", " handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
TransformerWrapper(transformer=MinMaxScaler())
MinMaxScaler()
MinMaxScaler()
DecisionTreeRegressor(random_state=123)
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "1780.4708 | \n", "18661626.9065 | \n", "4319.9105 | \n", "0.8885 | \n", "0.3492 | \n", "0.1282 | \n", "
1 | \n", "2378.8326 | \n", "34060507.0214 | \n", "5836.1380 | \n", "0.8020 | \n", "0.4405 | \n", "0.1317 | \n", "
2 | \n", "1914.8876 | \n", "23340496.9688 | \n", "4831.2004 | \n", "0.7045 | \n", "0.4900 | \n", "0.1479 | \n", "
3 | \n", "1965.2661 | \n", "22365357.2218 | \n", "4729.2026 | \n", "0.8133 | \n", "0.3707 | \n", "0.1321 | \n", "
4 | \n", "2391.0387 | \n", "30760382.6717 | \n", "5546.2043 | \n", "0.7712 | \n", "0.5373 | \n", "0.1991 | \n", "
5 | \n", "1906.3528 | \n", "20367865.5342 | \n", "4513.0772 | \n", "0.8690 | \n", "0.3184 | \n", "0.1080 | \n", "
6 | \n", "1729.7143 | \n", "21351600.2575 | \n", "4620.7792 | \n", "0.8547 | \n", "0.3387 | \n", "0.1147 | \n", "
7 | \n", "2039.0614 | \n", "26615466.7325 | \n", "5159.0180 | \n", "0.8536 | \n", "0.4689 | \n", "0.1314 | \n", "
8 | \n", "1927.4966 | \n", "22598678.1282 | \n", "4753.8067 | \n", "0.8540 | \n", "0.3663 | \n", "0.1316 | \n", "
9 | \n", "2195.4010 | \n", "27341573.0667 | \n", "5228.9170 | \n", "0.8239 | \n", "0.4443 | \n", "0.1612 | \n", "
Mean | \n", "2022.8522 | \n", "24746355.4509 | \n", "4953.8254 | \n", "0.8235 | \n", "0.4124 | \n", "0.1386 | \n", "
Std | \n", "217.8612 | \n", "4616581.4311 | \n", "453.8386 | \n", "0.0516 | \n", "0.0698 | \n", "0.0246 | \n", "
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "2591.8970 | \n", "23266281.4574 | \n", "4823.5134 | \n", "0.8610 | \n", "0.4637 | \n", "0.2976 | \n", "
1 | \n", "2863.6017 | \n", "30202461.8149 | \n", "5495.6766 | \n", "0.8244 | \n", "0.4882 | \n", "0.3053 | \n", "
2 | \n", "2736.5380 | \n", "24936511.9328 | \n", "4993.6472 | \n", "0.6843 | \n", "0.5148 | \n", "0.3293 | \n", "
3 | \n", "2945.2626 | \n", "27479881.3264 | \n", "5242.1256 | \n", "0.7705 | \n", "0.5164 | \n", "0.4187 | \n", "
4 | \n", "3075.1990 | \n", "30901342.4317 | \n", "5558.8976 | \n", "0.7702 | \n", "0.5670 | \n", "0.3906 | \n", "
5 | \n", "2866.8198 | \n", "25117097.4494 | \n", "5011.6961 | \n", "0.8384 | \n", "0.3711 | \n", "0.2607 | \n", "
6 | \n", "2568.9545 | \n", "22780849.6859 | \n", "4772.9288 | \n", "0.8450 | \n", "0.3730 | \n", "0.2717 | \n", "
7 | \n", "2639.4091 | \n", "26044331.1073 | \n", "5103.3647 | \n", "0.8568 | \n", "0.4710 | \n", "0.2506 | \n", "
8 | \n", "2364.6343 | \n", "19889092.4425 | \n", "4459.7189 | \n", "0.8715 | \n", "0.4108 | \n", "0.3040 | \n", "
9 | \n", "2820.2231 | \n", "31860942.5716 | \n", "5644.5498 | \n", "0.7948 | \n", "0.4605 | \n", "0.2805 | \n", "
Mean | \n", "2747.2539 | \n", "26247879.2220 | \n", "5110.6119 | \n", "0.8117 | \n", "0.4636 | \n", "0.3109 | \n", "
Std | \n", "198.4899 | \n", "3670176.0781 | \n", "359.8965 | \n", "0.0547 | \n", "0.0603 | \n", "0.0521 | \n", "
BaggingRegressor(base_estimator=DecisionTreeRegressor(random_state=123),\n", " random_state=123)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
BaggingRegressor(base_estimator=DecisionTreeRegressor(random_state=123),\n", " random_state=123)
DecisionTreeRegressor(random_state=123)
DecisionTreeRegressor(random_state=123)
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "2054.7669 | \n", "26692081.4248 | \n", "5166.4380 | \n", "0.8406 | \n", "0.4131 | \n", "0.1936 | \n", "
1 | \n", "1991.3291 | \n", "27836623.8370 | \n", "5276.0424 | \n", "0.8381 | \n", "0.3933 | \n", "0.1198 | \n", "
2 | \n", "2577.5202 | \n", "34350249.6820 | \n", "5860.9086 | \n", "0.5651 | \n", "0.5748 | \n", "0.3238 | \n", "
3 | \n", "2408.3449 | \n", "30508533.2842 | \n", "5523.4530 | \n", "0.7453 | \n", "0.5172 | \n", "0.3788 | \n", "
4 | \n", "2564.6923 | \n", "31138720.3012 | \n", "5580.2079 | \n", "0.7684 | \n", "0.5678 | \n", "0.3023 | \n", "
5 | \n", "3145.5626 | \n", "39513518.5950 | \n", "6285.9779 | \n", "0.7458 | \n", "0.4481 | \n", "0.2825 | \n", "
6 | \n", "2069.4535 | \n", "27352438.4443 | \n", "5229.9559 | \n", "0.8139 | \n", "0.3412 | \n", "0.1427 | \n", "
7 | \n", "2125.2695 | \n", "26494689.4475 | \n", "5147.2992 | \n", "0.8543 | \n", "0.4403 | \n", "0.1571 | \n", "
8 | \n", "2053.8316 | \n", "21762810.2356 | \n", "4665.0627 | \n", "0.8594 | \n", "0.3307 | \n", "0.1743 | \n", "
9 | \n", "2440.6761 | \n", "29911998.0044 | \n", "5469.1862 | \n", "0.8073 | \n", "0.4988 | \n", "0.2361 | \n", "
Mean | \n", "2343.1447 | \n", "29556166.3256 | \n", "5420.4532 | \n", "0.7838 | \n", "0.4525 | \n", "0.2311 | \n", "
Std | \n", "342.7342 | \n", "4602124.0816 | \n", "418.1548 | \n", "0.0833 | \n", "0.0819 | \n", "0.0827 | \n", "
AdaBoostRegressor(base_estimator=DecisionTreeRegressor(random_state=123),\n", " n_estimators=10, random_state=123)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AdaBoostRegressor(base_estimator=DecisionTreeRegressor(random_state=123),\n", " n_estimators=10, random_state=123)
DecisionTreeRegressor(random_state=123)
DecisionTreeRegressor(random_state=123)
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "2720.8934 | \n", "22050841.6103 | \n", "4695.8324 | \n", "0.8683 | \n", "0.4637 | \n", "0.3425 | \n", "
1 | \n", "2865.4018 | \n", "30821460.9279 | \n", "5551.7079 | \n", "0.8208 | \n", "0.4535 | \n", "0.2800 | \n", "
2 | \n", "2581.6067 | \n", "22252661.3019 | \n", "4717.2727 | \n", "0.7183 | \n", "0.5463 | \n", "0.3794 | \n", "
3 | \n", "2810.2333 | \n", "21734211.3564 | \n", "4661.9965 | \n", "0.8185 | \n", "0.4864 | \n", "0.3873 | \n", "
4 | \n", "3070.0103 | \n", "30740150.2464 | \n", "5544.3801 | \n", "0.7714 | \n", "0.5469 | \n", "0.3679 | \n", "
5 | \n", "2854.7097 | \n", "22065332.3136 | \n", "4697.3750 | \n", "0.8581 | \n", "0.3771 | \n", "0.2778 | \n", "
6 | \n", "2450.8238 | \n", "20209907.9911 | \n", "4495.5431 | \n", "0.8625 | \n", "0.3740 | \n", "0.2901 | \n", "
7 | \n", "2595.2491 | \n", "23563676.7364 | \n", "4854.2432 | \n", "0.8704 | \n", "0.3997 | \n", "0.2334 | \n", "
8 | \n", "2262.9477 | \n", "18038706.9975 | \n", "4247.1999 | \n", "0.8835 | \n", "0.3846 | \n", "0.2937 | \n", "
9 | \n", "2947.5441 | \n", "28438703.1674 | \n", "5332.7951 | \n", "0.8168 | \n", "0.5125 | \n", "0.3660 | \n", "
Mean | \n", "2715.9420 | \n", "23991565.2649 | \n", "4879.8346 | \n", "0.8289 | \n", "0.4545 | \n", "0.3218 | \n", "
Std | \n", "231.8660 | \n", "4212892.9107 | \n", "422.8235 | \n", "0.0491 | \n", "0.0647 | \n", "0.0504 | \n", "
VotingRegressor(estimators=[('Gradient Boosting Regressor',\n", " GradientBoostingRegressor(random_state=123)),\n", " ('Random Forest Regressor',\n", " RandomForestRegressor(n_jobs=-1,\n", " random_state=123)),\n", " ('Extra Trees Regressor',\n", " ExtraTreesRegressor(n_jobs=-1, random_state=123))],\n", " n_jobs=-1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
VotingRegressor(estimators=[('Gradient Boosting Regressor',\n", " GradientBoostingRegressor(random_state=123)),\n", " ('Random Forest Regressor',\n", " RandomForestRegressor(n_jobs=-1,\n", " random_state=123)),\n", " ('Extra Trees Regressor',\n", " ExtraTreesRegressor(n_jobs=-1, random_state=123))],\n", " n_jobs=-1)
GradientBoostingRegressor(random_state=123)
RandomForestRegressor(n_jobs=-1, random_state=123)
ExtraTreesRegressor(n_jobs=-1, random_state=123)
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "2609.6884 | \n", "19941923.0191 | \n", "4465.6380 | \n", "0.8809 | \n", "0.4388 | \n", "0.3216 | \n", "
1 | \n", "2980.4083 | \n", "31017277.7854 | \n", "5569.3157 | \n", "0.8197 | \n", "0.4751 | \n", "0.2922 | \n", "
2 | \n", "2546.1494 | \n", "22498470.1082 | \n", "4743.2552 | \n", "0.7151 | \n", "0.4924 | \n", "0.2973 | \n", "
3 | \n", "2847.5662 | \n", "21076820.8684 | \n", "4590.9499 | \n", "0.8240 | \n", "0.4727 | \n", "0.3775 | \n", "
4 | \n", "2921.5377 | \n", "28163259.1669 | \n", "5306.9067 | \n", "0.7905 | \n", "0.5215 | \n", "0.3215 | \n", "
5 | \n", "2677.5306 | \n", "19787391.3140 | \n", "4448.3021 | \n", "0.8727 | \n", "0.3998 | \n", "0.2686 | \n", "
6 | \n", "2369.6118 | \n", "20267877.6270 | \n", "4501.9860 | \n", "0.8621 | \n", "0.3340 | \n", "0.2382 | \n", "
7 | \n", "2693.0703 | \n", "24841785.8067 | \n", "4984.1535 | \n", "0.8634 | \n", "0.4340 | \n", "0.2560 | \n", "
8 | \n", "2229.6840 | \n", "17762684.4081 | \n", "4214.5800 | \n", "0.8853 | \n", "0.3765 | \n", "0.2832 | \n", "
9 | \n", "3001.6387 | \n", "27582761.2165 | \n", "5251.9293 | \n", "0.8223 | \n", "0.5155 | \n", "0.3513 | \n", "
Mean | \n", "2687.6885 | \n", "23294025.1320 | \n", "4807.7016 | \n", "0.8336 | \n", "0.4460 | \n", "0.3007 | \n", "
Std | \n", "245.2930 | \n", "4159112.4380 | \n", "424.2995 | \n", "0.0494 | \n", "0.0582 | \n", "0.0408 | \n", "
StackingRegressor(cv=5,\n", " estimators=[('Gradient Boosting Regressor',\n", " GradientBoostingRegressor(random_state=123)),\n", " ('Random Forest Regressor',\n", " RandomForestRegressor(n_jobs=-1,\n", " random_state=123)),\n", " ('Extra Trees Regressor',\n", " ExtraTreesRegressor(n_jobs=-1,\n", " random_state=123))],\n", " final_estimator=LinearRegression(n_jobs=-1), n_jobs=-1,\n", " passthrough=True)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
StackingRegressor(cv=5,\n", " estimators=[('Gradient Boosting Regressor',\n", " GradientBoostingRegressor(random_state=123)),\n", " ('Random Forest Regressor',\n", " RandomForestRegressor(n_jobs=-1,\n", " random_state=123)),\n", " ('Extra Trees Regressor',\n", " ExtraTreesRegressor(n_jobs=-1,\n", " random_state=123))],\n", " final_estimator=LinearRegression(n_jobs=-1), n_jobs=-1,\n", " passthrough=True)
GradientBoostingRegressor(random_state=123)
RandomForestRegressor(n_jobs=-1, random_state=123)
ExtraTreesRegressor(n_jobs=-1, random_state=123)
LinearRegression(n_jobs=-1)
\n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|
Fold | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "2896.4964 | \n", "23611929.4013 | \n", "4859.2108 | \n", "0.8590 | \n", "0.5993 | \n", "0.3808 | \n", "
1 | \n", "3075.9419 | \n", "30047230.7486 | \n", "5481.5354 | \n", "0.8253 | \n", "0.4622 | \n", "0.3295 | \n", "
2 | \n", "3096.0185 | \n", "27757739.1878 | \n", "5268.5614 | \n", "0.6486 | \n", "0.6159 | \n", "0.4550 | \n", "
3 | \n", "3347.3144 | \n", "26993115.8247 | \n", "5195.4900 | \n", "0.7746 | \n", "0.8106 | \n", "0.5509 | \n", "
4 | \n", "3263.7660 | \n", "29391206.7843 | \n", "5421.3658 | \n", "0.7814 | \n", "0.5628 | \n", "0.3846 | \n", "
5 | \n", "2922.5372 | \n", "21672554.7596 | \n", "4655.3791 | \n", "0.8606 | \n", "0.4170 | \n", "0.2881 | \n", "
6 | \n", "2733.8071 | \n", "21012815.3865 | \n", "4583.9738 | \n", "0.8570 | \n", "0.3991 | \n", "0.3139 | \n", "
7 | \n", "2865.5796 | \n", "25843408.7132 | \n", "5083.6413 | \n", "0.8579 | \n", "0.5027 | \n", "0.2995 | \n", "
8 | \n", "2715.5680 | \n", "21671018.5929 | \n", "4655.2141 | \n", "0.8600 | \n", "0.5233 | \n", "0.4192 | \n", "
9 | \n", "3101.8547 | \n", "27472226.4139 | \n", "5241.3955 | \n", "0.8230 | \n", "0.5521 | \n", "0.3621 | \n", "
Mean | \n", "3001.8884 | \n", "25547324.5813 | \n", "5044.5767 | \n", "0.8147 | \n", "0.5445 | \n", "0.3784 | \n", "
Std | \n", "200.5163 | \n", "3164504.0885 | \n", "315.5478 | \n", "0.0635 | \n", "0.1121 | \n", "0.0765 | \n", "
\n", " | Model Name | \n", "Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
---|---|---|---|---|---|---|---|---|
Index | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
0 | \n", "Linear Regression | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "4312.6186 | \n", "3.845275e+07 | \n", "6182.4796 | \n", "0.7298 | \n", "0.6285 | \n", "0.4460 | \n", "
1 | \n", "Lasso Regression | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "4302.2469 | \n", "3.838653e+07 | \n", "6176.4463 | \n", "0.7306 | \n", "0.5913 | \n", "0.4430 | \n", "
2 | \n", "Ridge Regression | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "4296.0642 | \n", "3.839300e+07 | \n", "6176.6160 | \n", "0.7308 | \n", "0.5710 | \n", "0.4397 | \n", "
3 | \n", "Elastic Net | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "7571.4598 | \n", "1.047380e+08 | \n", "10182.3291 | \n", "0.2846 | \n", "0.8954 | \n", "1.2888 | \n", "
4 | \n", "Least Angle Regression | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "4303.5559 | \n", "3.838806e+07 | \n", "6176.5920 | \n", "0.7306 | \n", "0.5949 | \n", "0.4433 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
61 | \n", "Decision Tree Regressor | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "2343.1447 | \n", "2.955617e+07 | \n", "5420.4532 | \n", "0.7838 | \n", "0.4525 | \n", "0.2311 | \n", "
62 | \n", "Voting Regressor | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "2715.9420 | \n", "2.399157e+07 | \n", "4879.8346 | \n", "0.8289 | \n", "0.4545 | \n", "0.3218 | \n", "
63 | \n", "Stacking Regressor | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "2687.6885 | \n", "2.329403e+07 | \n", "4807.7016 | \n", "0.8336 | \n", "0.4460 | \n", "0.3007 | \n", "
64 | \n", "Stacking Regressor | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "2687.6885 | \n", "2.329403e+07 | \n", "4807.7016 | \n", "0.8336 | \n", "0.4460 | \n", "0.3007 | \n", "
65 | \n", "Light Gradient Boosting Machine | \n", "(TransformerWrapper(include=['age', 'bmi', 'ch... | \n", "3001.8884 | \n", "2.554732e+07 | \n", "5044.5767 | \n", "0.8147 | \n", "0.5445 | \n", "0.3784 | \n", "
66 rows × 8 columns
\n", "Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n", " ['trained_model', LinearRegression(n_jobs=-1)]])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n", " ['trained_model', LinearRegression(n_jobs=-1)]])
TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))
SimpleImputer(strategy='most_frequent')
SimpleImputer(strategy='most_frequent')
TransformerWrapper(include=['sex', 'smoker'],\n", " transformer=OrdinalEncoder(cols=['sex', 'smoker'],\n", " handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
TransformerWrapper(transformer=MinMaxScaler())
MinMaxScaler()
MinMaxScaler()
LinearRegression(n_jobs=-1)
DecisionTreeRegressor(max_depth=4, random_state=123)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
DecisionTreeRegressor(max_depth=4, random_state=123)
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n", " ('actual_estimator',\n", " GradientBoostingRegressor(random_state=123))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n", " ('actual_estimator',\n", " GradientBoostingRegressor(random_state=123))])
TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))
SimpleImputer(strategy='most_frequent')
SimpleImputer(strategy='most_frequent')
TransformerWrapper(include=['sex', 'smoker'],\n", " transformer=OrdinalEncoder(cols=['sex', 'smoker'],\n", " handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
TransformerWrapper(transformer=MinMaxScaler())
MinMaxScaler()
MinMaxScaler()
GradientBoostingRegressor(random_state=123)
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n", " ('trained_model', GradientBoostingRegressor(random_state=123))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(memory=FastMemory(location=C:\\Users\\owner\\AppData\\Local\\Temp\\joblib),\n", " steps=[('numerical_imputer',\n", " TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())),\n", " ('categorical_imputer',\n", " TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))),\n", " ('ordinal_encoding',\n", " TransformerW...\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))),\n", " ('onehot_encoding',\n", " TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))),\n", " ('normalize', TransformerWrapper(transformer=MinMaxScaler())),\n", " ('trained_model', GradientBoostingRegressor(random_state=123))])
TransformerWrapper(include=['age', 'bmi', 'children'],\n", " transformer=SimpleImputer())
SimpleImputer()
SimpleImputer()
TransformerWrapper(include=['sex', 'smoker', 'region'],\n", " transformer=SimpleImputer(strategy='most_frequent'))
SimpleImputer(strategy='most_frequent')
SimpleImputer(strategy='most_frequent')
TransformerWrapper(include=['sex', 'smoker'],\n", " transformer=OrdinalEncoder(cols=['sex', 'smoker'],\n", " handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1,\n", " 'female': 0,\n", " 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1,\n", " 'no': 0,\n", " 'yes': 1}}]))
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
OrdinalEncoder(cols=['sex', 'smoker'], handle_missing='return_nan',\n", " mapping=[{'col': 'sex',\n", " 'mapping': {nan: -1, 'female': 0, 'male': 1}},\n", " {'col': 'smoker',\n", " 'mapping': {nan: -1, 'no': 0, 'yes': 1}}])
TransformerWrapper(include=['region'],\n", " transformer=OneHotEncoder(cols=['region'],\n", " handle_missing='return_nan',\n", " use_cat_names=True))
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
OneHotEncoder(cols=['region'], handle_missing='return_nan', use_cat_names=True)
TransformerWrapper(transformer=MinMaxScaler())
MinMaxScaler()
MinMaxScaler()
GradientBoostingRegressor(random_state=123)
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "Session id | \n", "123 | \n", "
1 | \n", "Target | \n", "charges | \n", "
2 | \n", "Target type | \n", "Regression | \n", "
3 | \n", "Original data shape | \n", "(1338, 7) | \n", "
4 | \n", "Transformed data shape | \n", "(1338, 10) | \n", "
5 | \n", "Transformed train set shape | \n", "(936, 10) | \n", "
6 | \n", "Transformed test set shape | \n", "(402, 10) | \n", "
7 | \n", "Ordinal features | \n", "2 | \n", "
8 | \n", "Numeric features | \n", "3 | \n", "
9 | \n", "Categorical features | \n", "3 | \n", "
10 | \n", "Preprocess | \n", "True | \n", "
11 | \n", "Imputation type | \n", "simple | \n", "
12 | \n", "Numeric imputation | \n", "mean | \n", "
13 | \n", "Categorical imputation | \n", "mode | \n", "
14 | \n", "Maximum one-hot encoding | \n", "25 | \n", "
15 | \n", "Encoding method | \n", "None | \n", "
16 | \n", "Normalize | \n", "True | \n", "
17 | \n", "Normalize method | \n", "minmax | \n", "
18 | \n", "Fold Generator | \n", "KFold | \n", "
19 | \n", "Fold Number | \n", "10 | \n", "
20 | \n", "CPU Jobs | \n", "-1 | \n", "
21 | \n", "Use GPU | \n", "False | \n", "
22 | \n", "Log Experiment | \n", "False | \n", "
23 | \n", "Experiment Name | \n", "reg-default-name | \n", "
24 | \n", "USI | \n", "7443 | \n", "