\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age bmi sex_female children_0 children_1 children_2 \\\n",
"300 36.0 27.549999 0.0 0.0 0.0 0.0 \n",
"904 60.0 35.099998 1.0 1.0 0.0 0.0 \n",
"670 30.0 31.570000 0.0 0.0 0.0 0.0 \n",
"617 49.0 25.600000 0.0 0.0 0.0 1.0 \n",
"373 26.0 32.900002 0.0 0.0 0.0 1.0 \n",
"... ... ... ... ... ... ... \n",
"1238 37.0 22.705000 0.0 0.0 0.0 0.0 \n",
"1147 20.0 31.920000 1.0 1.0 0.0 0.0 \n",
"106 19.0 28.400000 1.0 0.0 1.0 0.0 \n",
"1041 18.0 23.084999 0.0 1.0 0.0 0.0 \n",
"1122 53.0 36.860001 1.0 0.0 0.0 0.0 \n",
"\n",
" children_3 children_4 children_5 smoker_yes region_northeast \\\n",
"300 1.0 0.0 0.0 0.0 1.0 \n",
"904 0.0 0.0 0.0 0.0 0.0 \n",
"670 1.0 0.0 0.0 0.0 0.0 \n",
"617 0.0 0.0 0.0 1.0 0.0 \n",
"373 0.0 0.0 0.0 1.0 0.0 \n",
"... ... ... ... ... ... \n",
"1238 1.0 0.0 0.0 0.0 1.0 \n",
"1147 0.0 0.0 0.0 0.0 0.0 \n",
"106 0.0 0.0 0.0 0.0 0.0 \n",
"1041 0.0 0.0 0.0 0.0 1.0 \n",
"1122 1.0 0.0 0.0 1.0 0.0 \n",
"\n",
" region_northwest region_southeast region_southwest \n",
"300 0.0 0.0 0.0 \n",
"904 0.0 0.0 1.0 \n",
"670 0.0 1.0 0.0 \n",
"617 0.0 0.0 1.0 \n",
"373 0.0 0.0 1.0 \n",
"... ... ... ... \n",
"1238 0.0 0.0 0.0 \n",
"1147 1.0 0.0 0.0 \n",
"106 0.0 0.0 1.0 \n",
"1041 0.0 0.0 0.0 \n",
"1122 1.0 0.0 0.0 \n",
"\n",
"[936 rows x 14 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check transformed X_train\n",
"get_config('X_train')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['age', 'bmi', 'sex_female', 'children_0', 'children_1', 'children_2',\n",
" 'children_3', 'children_4', 'children_5', 'smoker_yes',\n",
" 'region_northeast', 'region_northwest', 'region_southeast',\n",
" 'region_southwest'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# list columns of transformed X_train \n",
"get_config('X_train').columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 👉Model Training & Selection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare Models"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"| \n", " | age | \n", "bmi | \n", "sex_female | \n", "children_0 | \n", "children_1 | \n", "children_2 | \n", "children_3 | \n", "children_4 | \n", "children_5 | \n", "smoker_yes | \n", "region_northeast | \n", "region_northwest | \n", "region_southeast | \n", "region_southwest | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 300 | \n", "36.0 | \n", "27.549999 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
| 904 | \n", "60.0 | \n", "35.099998 | \n", "1.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "
| 670 | \n", "30.0 | \n", "31.570000 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "
| 617 | \n", "49.0 | \n", "25.600000 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "
| 373 | \n", "26.0 | \n", "32.900002 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "
| ... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
| 1238 | \n", "37.0 | \n", "22.705000 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
| 1147 | \n", "20.0 | \n", "31.920000 | \n", "1.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "
| 106 | \n", "19.0 | \n", "28.400000 | \n", "1.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "
| 1041 | \n", "18.0 | \n", "23.084999 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
| 1122 | \n", "53.0 | \n", "36.860001 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "
936 rows × 14 columns
\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", "2702.7680 | \n", "23242056.4409 | \n", "4801.5704 | \n", "0.8348 | \n", "0.4397 | \n", "0.3113 | \n", "0.0270 | \n", "
| rf | \n", "Random Forest Regressor | \n", "2736.7455 | \n", "24862762.2305 | \n", "4970.6959 | \n", "0.8213 | \n", "0.4674 | \n", "0.3294 | \n", "0.1110 | \n", "
| catboost | \n", "CatBoost Regressor | \n", "2865.4462 | \n", "25334554.7726 | \n", "5017.8574 | \n", "0.8193 | \n", "0.4774 | \n", "0.3419 | \n", "0.4420 | \n", "
| lightgbm | \n", "Light Gradient Boosting Machine | \n", "2959.5584 | \n", "25236477.0456 | \n", "5013.0892 | \n", "0.8171 | \n", "0.5427 | \n", "0.3685 | \n", "0.0930 | \n", "
| ada | \n", "AdaBoost Regressor | \n", "4162.2323 | \n", "28328260.0955 | \n", "5316.6146 | \n", "0.7985 | \n", "0.6349 | \n", "0.7263 | \n", "0.0090 | \n", "
| et | \n", "Extra Trees Regressor | \n", "2814.2964 | \n", "28815493.0260 | \n", "5339.0879 | \n", "0.7964 | \n", "0.4889 | \n", "0.3350 | \n", "0.0990 | \n", "
| xgboost | \n", "Extreme Gradient Boosting | \n", "3302.3215 | \n", "31739266.6000 | \n", "5615.5941 | \n", "0.7701 | \n", "0.5661 | \n", "0.4218 | \n", "0.1400 | \n", "
| llar | \n", "Lasso Least Angle Regression | \n", "4315.7895 | \n", "38355976.5080 | \n", "6173.8740 | \n", "0.7311 | \n", "0.6105 | \n", "0.4415 | \n", "0.0050 | \n", "
| ridge | \n", "Ridge Regression | \n", "4336.2309 | \n", "38381496.8000 | \n", "6175.9541 | \n", "0.7309 | \n", "0.6193 | \n", "0.4454 | \n", "0.0050 | \n", "
| lr | \n", "Linear Regression | \n", "4323.6136 | \n", "38380061.2000 | \n", "6175.7164 | \n", "0.7308 | \n", "0.6175 | \n", "0.4432 | \n", "0.5930 | \n", "
| br | \n", "Bayesian Ridge | \n", "4333.6881 | \n", "38381669.3629 | \n", "6175.9476 | \n", "0.7308 | \n", "0.6151 | \n", "0.4450 | \n", "0.0060 | \n", "
| lasso | \n", "Lasso Regression | \n", "4323.0688 | \n", "38375137.8000 | \n", "6175.3801 | \n", "0.7308 | \n", "0.6140 | \n", "0.4431 | \n", "0.0060 | \n", "
| lar | \n", "Least Angle Regression | \n", "4450.2675 | \n", "39682987.5896 | \n", "6267.8924 | \n", "0.7232 | \n", "0.6467 | \n", "0.4693 | \n", "0.0060 | \n", "
| dt | \n", "Decision Tree Regressor | \n", "3148.3402 | \n", "43766011.6491 | \n", "6584.7198 | \n", "0.6855 | \n", "0.5331 | \n", "0.3455 | \n", "0.0070 | \n", "
| huber | \n", "Huber Regressor | \n", "3455.2997 | \n", "48908984.4059 | \n", "6971.2642 | \n", "0.6545 | \n", "0.4790 | \n", "0.2174 | \n", "0.0150 | \n", "
| omp | \n", "Orthogonal Matching Pursuit | \n", "5754.7768 | \n", "57503216.4290 | \n", "7566.7093 | \n", "0.5997 | \n", "0.7418 | \n", "0.8990 | \n", "0.0060 | \n", "
| par | \n", "Passive Aggressive Regressor | \n", "4164.7843 | \n", "61324373.4835 | \n", "7747.8332 | \n", "0.5840 | \n", "0.4724 | \n", "0.2586 | \n", "0.0070 | \n", "
| en | \n", "Elastic Net | \n", "7369.0573 | \n", "90443346.8000 | \n", "9468.6782 | \n", "0.3791 | \n", "0.7377 | \n", "0.9256 | \n", "0.0050 | \n", "
| knn | \n", "K Neighbors Regressor | \n", "7805.8425 | \n", "126951808.0000 | \n", "11221.6535 | \n", "0.1218 | \n", "0.8398 | \n", "0.9147 | \n", "0.0070 | \n", "
| \n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "3001.2294 | \n", "37001480.2590 | \n", "6082.8842 | \n", "0.7790 | \n", "0.4984 | \n", "0.3140 | \n", "
| 1 | \n", "3389.8885 | \n", "49305179.5732 | \n", "7021.7647 | \n", "0.7133 | \n", "0.5574 | \n", "0.3361 | \n", "
| 2 | \n", "2926.0191 | \n", "42025684.6666 | \n", "6482.7220 | \n", "0.4679 | \n", "0.6215 | \n", "0.4025 | \n", "
| 3 | \n", "2744.7144 | \n", "34078761.4507 | \n", "5837.7017 | \n", "0.7154 | \n", "0.5412 | \n", "0.3740 | \n", "
| 4 | \n", "3924.4816 | \n", "59489464.3207 | \n", "7712.9414 | \n", "0.5575 | \n", "0.6455 | \n", "0.4796 | \n", "
| 5 | \n", "3322.5435 | \n", "42747575.4453 | \n", "6538.1630 | \n", "0.7250 | \n", "0.4869 | \n", "0.2928 | \n", "
| 6 | \n", "3158.7047 | \n", "49369669.1652 | \n", "7026.3553 | \n", "0.6641 | \n", "0.4511 | \n", "0.3089 | \n", "
| 7 | \n", "2405.2970 | \n", "31318616.6440 | \n", "5596.3038 | \n", "0.8278 | \n", "0.4497 | \n", "0.1434 | \n", "
| 8 | \n", "3021.5461 | \n", "39091793.3775 | \n", "6252.3430 | \n", "0.7475 | \n", "0.5117 | \n", "0.4381 | \n", "
| 9 | \n", "3588.9772 | \n", "53231891.5889 | \n", "7296.0189 | \n", "0.6571 | \n", "0.5679 | \n", "0.3653 | \n", "
| Mean | \n", "3148.3402 | \n", "43766011.6491 | \n", "6584.7198 | \n", "0.6855 | \n", "0.5331 | \n", "0.3455 | \n", "
| SD | \n", "410.7953 | \n", "8481549.4829 | \n", "638.3390 | \n", "0.1005 | \n", "0.0631 | \n", "0.0878 | \n", "
| \n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "1710.0867 | \n", "18253568.8962 | \n", "4272.4196 | \n", "0.8910 | \n", "0.3435 | \n", "0.1349 | \n", "
| 1 | \n", "2342.9618 | \n", "33002910.7856 | \n", "5744.8160 | \n", "0.8081 | \n", "0.4462 | \n", "0.1421 | \n", "
| 2 | \n", "1992.6884 | \n", "23279759.5944 | \n", "4824.9103 | \n", "0.7053 | \n", "0.4672 | \n", "0.1580 | \n", "
| 3 | \n", "2250.2711 | \n", "25594847.8750 | \n", "5059.1351 | \n", "0.7863 | \n", "0.4246 | \n", "0.2126 | \n", "
| 4 | \n", "2157.4516 | \n", "24978154.4390 | \n", "4997.8150 | \n", "0.8142 | \n", "0.4363 | \n", "0.1531 | \n", "
| 5 | \n", "1991.3288 | \n", "18794342.2788 | \n", "4335.2442 | \n", "0.8791 | \n", "0.3399 | \n", "0.1565 | \n", "
| 6 | \n", "1688.3935 | \n", "20093049.8225 | \n", "4482.5272 | \n", "0.8633 | \n", "0.3137 | \n", "0.1210 | \n", "
| 7 | \n", "2060.8145 | \n", "26178263.6299 | \n", "5116.4698 | \n", "0.8561 | \n", "0.4613 | \n", "0.1332 | \n", "
| 8 | \n", "2088.2260 | \n", "23545921.7229 | \n", "4852.4140 | \n", "0.8479 | \n", "0.3741 | \n", "0.1592 | \n", "
| 9 | \n", "2233.1985 | \n", "27217915.9631 | \n", "5217.0793 | \n", "0.8247 | \n", "0.4302 | \n", "0.1662 | \n", "
| Mean | \n", "2051.5421 | \n", "24093873.5007 | \n", "4890.2830 | \n", "0.8276 | \n", "0.4037 | \n", "0.1537 | \n", "
| SD | \n", "206.3066 | \n", "4191347.7243 | \n", "423.0902 | \n", "0.0514 | \n", "0.0529 | \n", "0.0238 | \n", "
| \n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "1776.4799 | \n", "18836339.8037 | \n", "4340.0852 | \n", "0.8875 | \n", "0.3481 | \n", "0.1376 | \n", "
| 1 | \n", "2300.3532 | \n", "30644279.5547 | \n", "5535.7276 | \n", "0.8218 | \n", "0.4243 | \n", "0.1539 | \n", "
| 2 | \n", "1924.2258 | \n", "20323874.4793 | \n", "4508.2008 | \n", "0.7427 | \n", "0.4367 | \n", "0.1694 | \n", "
| 3 | \n", "2163.4615 | \n", "22106257.5369 | \n", "4701.7292 | \n", "0.8154 | \n", "0.3846 | \n", "0.1967 | \n", "
| 4 | \n", "2173.8514 | \n", "25725889.5417 | \n", "5072.0696 | \n", "0.8087 | \n", "0.4448 | \n", "0.1605 | \n", "
| 5 | \n", "2161.0406 | \n", "18397565.9304 | \n", "4289.2384 | \n", "0.8817 | \n", "0.3326 | \n", "0.1567 | \n", "
| 6 | \n", "1726.2661 | \n", "19731367.8977 | \n", "4442.0004 | \n", "0.8657 | \n", "0.3310 | \n", "0.1356 | \n", "
| 7 | \n", "2079.3486 | \n", "24270253.8962 | \n", "4926.4849 | \n", "0.8665 | \n", "0.4205 | \n", "0.1367 | \n", "
| 8 | \n", "1986.6675 | \n", "20824366.8000 | \n", "4563.3723 | \n", "0.8655 | \n", "0.3657 | \n", "0.1718 | \n", "
| 9 | \n", "2103.8869 | \n", "25488147.2315 | \n", "5048.5787 | \n", "0.8358 | \n", "0.4128 | \n", "0.1513 | \n", "
| Mean | \n", "2039.5582 | \n", "22634834.2672 | \n", "4742.7487 | \n", "0.8391 | \n", "0.3901 | \n", "0.1570 | \n", "
| SD | \n", "174.7402 | \n", "3663777.6927 | \n", "375.7245 | \n", "0.0417 | \n", "0.0412 | \n", "0.0180 | \n", "
| \n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "1684.9289 | \n", "18347418.5622 | \n", "4283.3887 | \n", "0.8904 | \n", "0.3422 | \n", "0.1300 | \n", "
| 1 | \n", "2268.6353 | \n", "31524193.3944 | \n", "5614.6410 | \n", "0.8167 | \n", "0.4271 | \n", "0.1416 | \n", "
| 2 | \n", "1926.8691 | \n", "21534874.6378 | \n", "4640.5684 | \n", "0.7273 | \n", "0.4481 | \n", "0.1605 | \n", "
| 3 | \n", "2159.7119 | \n", "23453407.4775 | \n", "4842.8718 | \n", "0.8042 | \n", "0.3988 | \n", "0.1923 | \n", "
| 4 | \n", "2139.0034 | \n", "25136877.9484 | \n", "5013.6691 | \n", "0.8130 | \n", "0.4384 | \n", "0.1554 | \n", "
| 5 | \n", "2052.6057 | \n", "17787615.2115 | \n", "4217.5366 | \n", "0.8856 | \n", "0.3296 | \n", "0.1540 | \n", "
| 6 | \n", "1677.8415 | \n", "19736734.1414 | \n", "4442.6044 | \n", "0.8657 | \n", "0.3187 | \n", "0.1243 | \n", "
| 7 | \n", "2027.8924 | \n", "25005479.8275 | \n", "5000.5480 | \n", "0.8625 | \n", "0.4340 | \n", "0.1293 | \n", "
| 8 | \n", "2014.0100 | \n", "21739953.6415 | \n", "4662.6123 | \n", "0.8596 | \n", "0.3648 | \n", "0.1631 | \n", "
| 9 | \n", "2135.5030 | \n", "26042026.2510 | \n", "5103.1389 | \n", "0.8323 | \n", "0.4166 | \n", "0.1543 | \n", "
| Mean | \n", "2008.7001 | \n", "23030858.1093 | \n", "4782.1579 | \n", "0.8357 | \n", "0.3918 | \n", "0.1505 | \n", "
| SD | \n", "186.2108 | \n", "3922992.8879 | \n", "402.2733 | \n", "0.0461 | \n", "0.0463 | \n", "0.0192 | \n", "
| \n", " | MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "2137.4349 | \n", "16935113.3177 | \n", "4115.2294 | \n", "0.8988 | \n", "0.3473 | \n", "0.2396 | \n", "
| 1 | \n", "2723.0075 | \n", "28256992.1076 | \n", "5315.7306 | \n", "0.8357 | \n", "0.4216 | \n", "0.2516 | \n", "
| 2 | \n", "2569.8877 | \n", "20111251.0308 | \n", "4484.5569 | \n", "0.7454 | \n", "0.4324 | \n", "0.2948 | \n", "
| 3 | \n", "2610.2103 | \n", "21629398.5772 | \n", "4650.7417 | \n", "0.8194 | \n", "0.4061 | \n", "0.3187 | \n", "
| 4 | \n", "2488.7336 | \n", "23836624.9526 | \n", "4882.2766 | \n", "0.8227 | \n", "0.4503 | \n", "0.2510 | \n", "
| 5 | \n", "2618.4610 | \n", "17348168.3992 | \n", "4165.1133 | \n", "0.8884 | \n", "0.3356 | \n", "0.2596 | \n", "
| 6 | \n", "2284.5336 | \n", "18712209.2319 | \n", "4325.7611 | \n", "0.8727 | \n", "0.3519 | \n", "0.2613 | \n", "
| 7 | \n", "2554.4604 | \n", "23822535.5081 | \n", "4880.8335 | \n", "0.8690 | \n", "0.4246 | \n", "0.2414 | \n", "
| 8 | \n", "2447.7611 | \n", "18035323.7785 | \n", "4246.8016 | \n", "0.8835 | \n", "0.3817 | \n", "0.3028 | \n", "
| 9 | \n", "2596.6187 | \n", "24474708.0900 | \n", "4947.1919 | \n", "0.8424 | \n", "0.4345 | \n", "0.2704 | \n", "
| Mean | \n", "2503.1109 | \n", "21316232.4994 | \n", "4601.4237 | \n", "0.8478 | \n", "0.3986 | \n", "0.2691 | \n", "
| SD | \n", "165.4495 | \n", "3532975.1686 | \n", "378.3288 | \n", "0.0432 | \n", "0.0393 | \n", "0.0258 | \n", "
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"interpret_model(tuned_dt)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interpret_model(tuned_dt, plot = 'reason', observation=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Predictions"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# predict on holdout / test set\n",
"pred_holdout = predict_model(best);"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
"Pipeline(memory=None,\n",
" steps=[('dtypes',\n",
" DataTypes_Auto_infer(categorical_features=[],\n",
" display_types=True, features_todrop=[],\n",
" id_columns=[], ml_usecase='regression',\n",
" numerical_features=[], target='charges',\n",
" time_features=[])),\n",
" ('imputer',\n",
" Simple_Imputer(categorical_strategy='not_available',\n",
" fill_value_categorical=None,\n",
" fill_value_numerical=None,\n",
" numeric_strategy...\n",
" learning_rate=0.1, loss='ls',\n",
" max_depth=3, max_features=None,\n",
" max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0,\n",
" min_impurity_split=None,\n",
" min_samples_leaf=1,\n",
" min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0,\n",
" n_estimators=100,\n",
" n_iter_no_change=None,\n",
" presort='deprecated',\n",
" random_state=123, subsample=1.0,\n",
" tol=0.0001, validation_fraction=0.1,\n",
" verbose=0, warm_start=False)]],\n",
" verbose=False)"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loaded_pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## THE END"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pycaret-new",
"language": "python",
"name": "pycaret-new"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
\n",
"
\n",
" "
],
"text/plain": [
"\n",
" Visualization omitted, Javascript library not loaded!
\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security. If you are using\n", " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", "
\n", " Have you run `initjs()` in this notebook? If this notebook was from another\n", " user you must also trust this notebook (File -> Trust notebook). If you are viewing\n", " this notebook on github the Javascript has been stripped for security. If you are using\n", " JupyterLab this error is because a JupyterLab extension has not yet been written.\n", "
| \n", " | Model | \n", "MAE | \n", "MSE | \n", "RMSE | \n", "R2 | \n", "RMSLE | \n", "MAPE | \n", "
|---|---|---|---|---|---|---|---|
| 0 | \n", "Gradient Boosting Regressor | \n", "2386.2018 | \n", "17296249.1379 | \n", "4158.8759 | \n", "0.8789 | \n", "0.3985 | \n", "0.2922 | \n", "
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age bmi sex_female children_0 children_1 children_2 \\\n",
"0 49.0 42.680000 1.0 0.0 0.0 1.0 \n",
"1 32.0 37.334999 0.0 0.0 1.0 0.0 \n",
"2 27.0 31.400000 1.0 1.0 0.0 0.0 \n",
"3 35.0 24.129999 0.0 0.0 1.0 0.0 \n",
"4 60.0 25.740000 0.0 1.0 0.0 0.0 \n",
"\n",
" children_3 children_4 children_5 smoker_yes region_northeast \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 1.0 \n",
"2 0.0 0.0 0.0 1.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" region_northwest region_southeast region_southwest charges \\\n",
"0 0.0 1.0 0.0 9800.888672 \n",
"1 0.0 0.0 0.0 4667.607422 \n",
"2 0.0 0.0 1.0 34838.871094 \n",
"3 1.0 0.0 0.0 5125.215820 \n",
"4 0.0 1.0 0.0 12142.578125 \n",
"\n",
" Label \n",
"0 10621.483595 \n",
"1 7290.151941 \n",
"2 36012.959871 \n",
"3 7553.788882 \n",
"4 14904.032497 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred_holdout.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | age | \n", "bmi | \n", "sex_female | \n", "children_0 | \n", "children_1 | \n", "children_2 | \n", "children_3 | \n", "children_4 | \n", "children_5 | \n", "smoker_yes | \n", "region_northeast | \n", "region_northwest | \n", "region_southeast | \n", "region_southwest | \n", "charges | \n", "Label | \n", "
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \n", "49.0 | \n", "42.680000 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "9800.888672 | \n", "10621.483595 | \n", "
| 1 | \n", "32.0 | \n", "37.334999 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "4667.607422 | \n", "7290.151941 | \n", "
| 2 | \n", "27.0 | \n", "31.400000 | \n", "1.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "34838.871094 | \n", "36012.959871 | \n", "
| 3 | \n", "35.0 | \n", "24.129999 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "5125.215820 | \n", "7553.788882 | \n", "
| 4 | \n", "60.0 | \n", "25.740000 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1.0 | \n", "0.0 | \n", "12142.578125 | \n", "14904.032497 | \n", "
\n",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age sex bmi children smoker region\n",
"0 19 female 27.900 0 yes southwest\n",
"1 18 male 33.770 1 no southeast\n",
"2 28 male 33.000 3 no southeast\n",
"3 33 male 22.705 0 no northwest\n",
"4 32 male 28.880 0 no northwest"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# predict on new data\n",
"data2 = data.copy()\n",
"data2.drop('charges', axis=1, inplace=True)\n",
"data2.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# finalize model\n",
"best_final = finalize_model(best)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| \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",
"\n",
"\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" age sex bmi children smoker region Label\n",
"0 19 female 27.900 0 yes southwest 18894.260073\n",
"1 18 male 33.770 1 no southeast 3698.287534\n",
"2 28 male 33.000 3 no southeast 6029.271578\n",
"3 33 male 22.705 0 no northwest 8958.189116\n",
"4 32 male 28.880 0 no northwest 3900.039002"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# predict on data2\n",
"predictions = predict_model(best_final, data=data2)\n",
"predictions.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 👉 Save / Load / Deploy Model"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transformation Pipeline and Model Successfully Saved\n"
]
},
{
"data": {
"text/plain": [
"(Pipeline(memory=None,\n",
" steps=[('dtypes',\n",
" DataTypes_Auto_infer(categorical_features=[],\n",
" display_types=True, features_todrop=[],\n",
" id_columns=[], ml_usecase='regression',\n",
" numerical_features=[], target='charges',\n",
" time_features=[])),\n",
" ('imputer',\n",
" Simple_Imputer(categorical_strategy='not_available',\n",
" fill_value_categorical=None,\n",
" fill_value_numerical=None,\n",
" numeric_strategy...\n",
" learning_rate=0.1, loss='ls',\n",
" max_depth=3, max_features=None,\n",
" max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0,\n",
" min_impurity_split=None,\n",
" min_samples_leaf=1,\n",
" min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0,\n",
" n_estimators=100,\n",
" n_iter_no_change=None,\n",
" presort='deprecated',\n",
" random_state=123, subsample=1.0,\n",
" tol=0.0001, validation_fraction=0.1,\n",
" verbose=0, warm_start=False)]],\n",
" verbose=False),\n",
" 'insurance-pipeline.pkl')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"save_model(best_final, 'insurance-pipeline')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Transformation Pipeline and Model Successfully Loaded\n"
]
}
],
"source": [
"loaded_pipeline = load_model('insurance-pipeline')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pipeline(memory=None,\n",
" steps=[('dtypes',\n",
" DataTypes_Auto_infer(categorical_features=[],\n",
" display_types=True, features_todrop=[],\n",
" id_columns=[], ml_usecase='regression',\n",
" numerical_features=[], target='charges',\n",
" time_features=[])),\n",
" ('imputer',\n",
" Simple_Imputer(categorical_strategy='not_available',\n",
" fill_value_categorical=None,\n",
" fill_value_numerical=None,\n",
" numeric_strategy...\n",
" learning_rate=0.1, loss='ls',\n",
" max_depth=3, max_features=None,\n",
" max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0,\n",
" min_impurity_split=None,\n",
" min_samples_leaf=1,\n",
" min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0,\n",
" n_estimators=100,\n",
" n_iter_no_change=None,\n",
" presort='deprecated',\n",
" random_state=123, subsample=1.0,\n",
" tol=0.0001, validation_fraction=0.1,\n",
" verbose=0, warm_start=False)]],\n",
" verbose=False)\n"
]
}
],
"source": [
"print(loaded_pipeline)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# visualize pipeline\n",
"from sklearn import set_config\n",
"set_config(display = 'diagram')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| \n", " | age | \n", "sex | \n", "bmi | \n", "children | \n", "smoker | \n", "region | \n", "Label | \n", "
|---|---|---|---|---|---|---|---|
| 0 | \n", "19 | \n", "female | \n", "27.900 | \n", "0 | \n", "yes | \n", "southwest | \n", "18894.260073 | \n", "
| 1 | \n", "18 | \n", "male | \n", "33.770 | \n", "1 | \n", "no | \n", "southeast | \n", "3698.287534 | \n", "
| 2 | \n", "28 | \n", "male | \n", "33.000 | \n", "3 | \n", "no | \n", "southeast | \n", "6029.271578 | \n", "
| 3 | \n", "33 | \n", "male | \n", "22.705 | \n", "0 | \n", "no | \n", "northwest | \n", "8958.189116 | \n", "
| 4 | \n", "32 | \n", "male | \n", "28.880 | \n", "0 | \n", "no | \n", "northwest | \n", "3900.039002 | \n", "
Pipeline(memory=None,\n",
" steps=[('dtypes',\n",
" DataTypes_Auto_infer(categorical_features=[],\n",
" display_types=True, features_todrop=[],\n",
" id_columns=[], ml_usecase='regression',\n",
" numerical_features=[], target='charges',\n",
" time_features=[])),\n",
" ('imputer',\n",
" Simple_Imputer(categorical_strategy='not_available',\n",
" fill_value_categorical=None,\n",
" fill_value_numerical=None,\n",
" numeric_strategy...\n",
" learning_rate=0.1, loss='ls',\n",
" max_depth=3, max_features=None,\n",
" max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0,\n",
" min_impurity_split=None,\n",
" min_samples_leaf=1,\n",
" min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0,\n",
" n_estimators=100,\n",
" n_iter_no_change=None,\n",
" presort='deprecated',\n",
" random_state=123, subsample=1.0,\n",
" tol=0.0001, validation_fraction=0.1,\n",
" verbose=0, warm_start=False)]],\n",
" verbose=False)DataTypes_Auto_infer(ml_usecase='regression', target='charges')
Simple_Imputer(categorical_strategy='not_available',\n",
" fill_value_categorical=None, fill_value_numerical=None,\n",
" numeric_strategy='mean', target_variable=None)New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
" target='charges')passthrough
passthrough
passthrough
passthrough
New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
" target='charges')Make_Time_Features(list_of_features=None,\n",
" time_feature=Index([], dtype='object'))passthrough
passthrough
passthrough
passthrough
passthrough
passthrough
passthrough
Dummify(target='charges')
Remove_100(target='charges')
Clean_Colum_Names()
passthrough
passthrough
passthrough
passthrough
GradientBoostingRegressor(random_state=123)