{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson 6 - Model interpretability\n",
    "\n",
    "> How to interpret the predictions from Random Forest models and use these insights to prune the feature space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/lewtun/dslectures/master?urlpath=lab/tree/notebooks%2Flesson06_model-interpretation.ipynb) \n",
    "[![slides](https://img.shields.io/static/v1?label=slides&message=lesson06_model-interpretation.pdf&color=blue&logo=Google-drive)](https://drive.google.com/open?id=15IIYC_MksmXI6VfSL3ee-rLm7TRF0v2U)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Understand how to interpret feature importance plots for Random Forest models.\n",
    "* Know how to drop uninformative features to build simpler models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This lesson is adapted (with permission) from Jeremy Howard's fantastic online course [_Introduction to Machine Learning for Coders_](https://course18.fast.ai/ml), in particular:\n",
    "\n",
    "* [3 — Performance, validation and model interpretation](https://course18.fast.ai/lessonsml1/lesson3.html)\n",
    "\n",
    "Below are a few relevant articles that may be of general interest:\n",
    "\n",
    "* [Explaining Feature Importance by example of a Random Forest](https://towardsdatascience.com/explaining-feature-importance-by-example-of-a-random-forest-d9166011959e)\n",
    "* [Beware Default Random Forest Importances](https://explained.ai/rf-importance/index.html)\n",
    "* [Explainable AI won’t deliver. Here’s why.](https://hackernoon.com/explainable-ai-wont-deliver-here-s-why-6738f54216be)\n",
    "* [Confidence Intervals](https://dfrieds.com/math/confidence-intervals.html)\n",
    "* [Reading and Writing Files in Python (Guide)](https://realpython.com/read-write-files-python/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework\n",
    "\n",
    "* Solve the exercises included in this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lesson we will analyse the preprocessed table of clean housing data and their addresses that we prepared in lesson 3:\n",
    "\n",
    "* `housing_processed.csv`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is model interpretability?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center\">\n",
    "<img src='images/interpretable.jpg' width='300'>\n",
    "    <p style=\"text-align: center;\"> <b>Figure reference:</b> https://bit.ly/3djjWc6 </p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A nice explanation for what it means to interpret a model's predictions is given in the _Beware Default Random Forest Importances_ article:\n",
    "\n",
    "> Training a model that accurately predicts outcomes is great, but most of the time you don't just need predictions, you want to be able to interpret your model. For example, if you build a model of house prices, knowing which features are most predictive of price tells us which features people are willing to pay for.\n",
    "\n",
    "In this lesson we will focus on one specific aspect of interpretability for Random Forests, namely _feature importance_ which is a technique that (with care) can be used to identify the most informative features in a dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload modules before executing user code\n",
    "%load_ext autoreload\n",
    "# reload all modules every time before executing Python code\n",
    "%autoreload 2\n",
    "# render plots in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to update the library if working locally\n",
    "# !pip install dslectures --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from dslectures.core import get_dataset, convert_strings_to_categories, rmse, fill_missing_values_with_median\n",
    "from pathlib import Path\n",
    "\n",
    "# data viz\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "sns.set_palette(sns.color_palette(\"muted\"))\n",
    "\n",
    "# ml magic\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "import scipy\n",
    "from scipy.cluster import hierarchy as hc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual we can download the dataset with our helper function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download of housing_processed.csv dataset complete.\n"
     ]
    }
   ],
   "source": [
    "get_dataset('housing_processed.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also make use of the `pathlib` library to handle our filepaths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "housing.csv                  imdb.csv\n",
      "housing_addresses.csv        keep_cols.npy\n",
      "housing_columns_to_keep.npy  submission.csv\n",
      "housing_gmaps_data_raw.csv   test.csv\n",
      "housing_merged.csv           train.csv\n",
      "housing_model.pkl            uc\n",
      "housing_processed.csv        word2vec-google-news-300.pkl\n"
     ]
    }
   ],
   "source": [
    "DATA = Path('../data/')\n",
    "!ls {DATA}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>city</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>bedrooms_per_household</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
       "      <th>ocean_proximity_NEAR BAY</th>\n",
       "      <th>ocean_proximity_NEAR OCEAN</th>\n",
       "      <th>ocean_proximity_ISLAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>69</td>\n",
       "      <td>94705</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>0.146591</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>620</td>\n",
       "      <td>94611</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>0.155797</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>620</td>\n",
       "      <td>94618</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>0.129516</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>620</td>\n",
       "      <td>94618</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>0.184458</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>620</td>\n",
       "      <td>94618</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>0.172096</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value  city  \\\n",
       "0       322.0       126.0         8.3252            452600.0    69   \n",
       "1      2401.0      1138.0         8.3014            358500.0   620   \n",
       "2       496.0       177.0         7.2574            352100.0   620   \n",
       "3       558.0       219.0         5.6431            341300.0   620   \n",
       "4       565.0       259.0         3.8462            342200.0   620   \n",
       "\n",
       "   postal_code  rooms_per_household  bedrooms_per_household  \\\n",
       "0        94705             6.984127                1.023810   \n",
       "1        94611             6.238137                0.971880   \n",
       "2        94618             8.288136                1.073446   \n",
       "3        94618             5.817352                1.073059   \n",
       "4        94618             6.281853                1.081081   \n",
       "\n",
       "   bedrooms_per_room  population_per_household  ocean_proximity_INLAND  \\\n",
       "0           0.146591                  2.555556                       0   \n",
       "1           0.155797                  2.109842                       0   \n",
       "2           0.129516                  2.802260                       0   \n",
       "3           0.184458                  2.547945                       0   \n",
       "4           0.172096                  2.181467                       0   \n",
       "\n",
       "   ocean_proximity_<1H OCEAN  ocean_proximity_NEAR BAY  \\\n",
       "0                          0                         1   \n",
       "1                          0                         1   \n",
       "2                          0                         1   \n",
       "3                          0                         1   \n",
       "4                          0                         1   \n",
       "\n",
       "   ocean_proximity_NEAR OCEAN  ocean_proximity_ISLAND  \n",
       "0                           0                       0  \n",
       "1                           0                       0  \n",
       "2                           0                       0  \n",
       "3                           0                       0  \n",
       "4                           0                       0  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_data = pd.read_csv(DATA/'housing_processed.csv'); housing_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data loaded, we can recreate our train/validation splits as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = housing_data.drop('median_house_value', axis=1)\n",
    "y = housing_data['median_house_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15554 train rows + 3889 valid rows\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print(f'{len(X_train)} train rows + {len(X_valid)} valid rows')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To simplify the evaluation of our models, we'll reuse our scoring function from lesson 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_rf_scores(fitted_model):\n",
    "    \"\"\"Generates RMSE and R^2 scores from fitted Random Forest model.\"\"\"\n",
    "\n",
    "    yhat_train = fitted_model.predict(X_train)\n",
    "    R2_train = fitted_model.score(X_train, y_train)\n",
    "    yhat_valid = fitted_model.predict(X_valid)\n",
    "    R2_valid = fitted_model.score(X_valid, y_valid)\n",
    "\n",
    "    scores = {\n",
    "        \"RMSE on train:\": rmse(y_train, yhat_train),\n",
    "        \"R^2 on train:\": R2_train,\n",
    "        \"RMSE on valid:\": rmse(y_valid, yhat_valid),\n",
    "        \"R^2 on valid:\": R2_valid,\n",
    "    }\n",
    "    if hasattr(fitted_model, \"oob_score_\"):\n",
    "        scores[\"OOB R^2:\"] = fitted_model.oob_score_\n",
    "\n",
    "    for score_name, score_value in scores.items():\n",
    "        print(score_name, round(score_value, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confidence intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that to make predictions with our Random Forest models, we take the _average value_ in each leaf node as we pass each row in the validation set through the tree. However, we would also like to estimate our _**confidence**_ in these predictions - how can we achieve this?\n",
    "\n",
    "One way to do this is to calculate the _**standard deviation of the predictions**_ of the trees. Conceptually, the idea is that if the standard deviation is high, each tree is generating very different predictions and may indicate the model has not learnt the most important features of the data.\n",
    "\n",
    "To get started, let's use our baseline model from the previous lesson:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on train: 16520.37\n",
      "R^2 on train: 0.971\n",
      "RMSE on valid: 42727.043\n",
      "R^2 on valid: 0.81\n",
      "OOB R^2: 0.791\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=40, max_features='sqrt', n_jobs=-1, oob_score=True, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "print_rf_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we concatenate all the predictions from each tree into a single array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(137125.0, 41845.23120978064)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds = np.stack([t.predict(X_valid) for t in model.estimators_])\n",
    "# calculate mean and standard deviation for single observation\n",
    "np.mean(preds[:,0]), np.std(preds[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 3889)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's create a copy of the validation dataset and add the mean predictions and their standard deviation as new columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Exercise #1\n",
    "\n",
    "* Combine `X_valid` and `y_valid` into a single array called `valid_copy`. You may find the `DataFrame.join(DataFrame)` method from pandas useful here.\n",
    "* Create two new columns `preds_mean` and `preds_std` that are the mean and standard deviation of the predictions for each tree in `preds`\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use these new columns to drill-down into the predictions of each individual, categorical feature. Let's examine `ocean_proximity_INLAND` which denotes whether a housing district is inland or not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='ocean_proximity_INLAND', data=valid_copy);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can calculate the predictions and standard deviation per category by applying a _**group by**_ operation in pandas, followed by taking the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>preds_mean</th>\n",
       "      <th>preds_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>227863.194178</td>\n",
       "      <td>227319.699349</td>\n",
       "      <td>51913.127856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>123654.224570</td>\n",
       "      <td>124241.488165</td>\n",
       "      <td>35858.872619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ocean_proximity_INLAND  median_house_value     preds_mean     preds_std\n",
       "0                       0       227863.194178  227319.699349  51913.127856\n",
       "1                       1       123654.224570  124241.488165  35858.872619"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['ocean_proximity_INLAND', 'median_house_value', 'preds_mean', 'preds_std']\n",
    "preds_quality = valid_copy[cols].groupby('ocean_proximity_INLAND', as_index=False).mean()\n",
    "preds_quality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table, we can see that the predictions and ground truth are close to each other on average, while the standard deviation varies somewhat for each category. We can visualise this table in terms of bar plots as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare figure \n",
    "fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, figsize=(12,7), sharex=True)\n",
    "\n",
    "# plot ground truth\n",
    "preds_quality.plot('ocean_proximity_INLAND', 'median_house_value', 'barh', ax=ax0)\n",
    "# put legend outside plot\n",
    "ax0.legend(loc='upper left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "# plot preds\n",
    "preds_quality.plot('ocean_proximity_INLAND', 'preds_mean', 'barh', xerr='preds_std', alpha=0.6, ax=ax1)\n",
    "# put legend outside plot\n",
    "ax1.legend(loc='upper left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error bars in the plot indicate how confident the model is at predicting each category.  Alternatively, we can compare the _distribution_ of values to inspect how close the predictions match the ground truth. For example, for housing districts where `ocean_proximity_INLAND` is 0 we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = valid_copy.copy().loc[valid_copy[\"ocean_proximity_INLAND\"] == 0]\n",
    "sample_mean = sample['preds_mean'].mean()\n",
    "sample_std = sample['preds_std'].std()\n",
    "lower_bound = sample_mean - sample_std\n",
    "upper_bound = sample_mean + sample_std\n",
    "\n",
    "sns.distplot(\n",
    "    sample[\"median_house_value\"], kde=False,\n",
    ")\n",
    "sns.distplot(sample[\"preds_mean\"], kde=False)\n",
    "plt.axvline(\n",
    "    x=sample_mean,\n",
    "    linestyle=\"--\",\n",
    "    linewidth=2.5,\n",
    "    label=\"Mean of predictions\",\n",
    "    c=\"k\",\n",
    ")\n",
    "plt.axvline(\n",
    "    x=lower_bound,\n",
    "    linestyle=\"--\",\n",
    "    linewidth=2.5,\n",
    "    label=\"Lower bound 68% CI\",\n",
    "    c=\"g\",\n",
    ")\n",
    "plt.axvline(\n",
    "    x=upper_bound,\n",
    "    linestyle=\"--\",\n",
    "    linewidth=2.5,\n",
    "    label=\"Upper bound 68% CI\",\n",
    "    c=\"purple\",\n",
    ")\n",
    "plt.legend(bbox_to_anchor=(1.01, 1), loc=\"upper left\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, we expect our models to perform best on categories that are most frequent in the data. One way to validate this hypothesis is by calculating the ratio of the standard deviation of the predictions to the predictions themselves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ocean_proximity_INLAND\n",
       "1    0.288622\n",
       "0    0.228371\n",
       "dtype: float64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_quality = valid_copy[cols].groupby(\"ocean_proximity_INLAND\", as_index=True).mean()\n",
    "(preds_quality[\"preds_std\"] / preds_quality[\"preds_mean\"]).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What the above tells us is that our predictions are less confident (i.e. higher variance) for housing districts that are inland - indeed looking at our bar plot we see these categories are under-represented in the data!\n",
    "\n",
    "In general, confidence intervals serve two main purposes:\n",
    "\n",
    "* We can identify which categories the model is less confident about and investigate further\n",
    "* We can identify which rows in the data the model is not confident about. This is particularly important when deploying models to production, where e.g. we need to decide how to evaluate the model's predictions for a _single_ housing district."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One drawback with the confidence interval analysis is that we need to drill-down into each feature to see where the model is making mistakes. In practice, we can get a global view by ranking each feature in terms of its importance to the model's predictions. In scikit-learn, the Random Forest model has an attribute called `feature_importances_` that we can use to rank each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rf_feature_importance(fitted_model, df):\n",
    "    return pd.DataFrame(\n",
    "        {\"Column\": df.columns, \"Importance\": fitted_model.feature_importances_}\n",
    "    ).sort_values(\"Importance\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use this function to calculate the feature importance for our fitted model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>median_income</td>\n",
       "      <td>0.248165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ocean_proximity_INLAND</td>\n",
       "      <td>0.135266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>population_per_household</td>\n",
       "      <td>0.088856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>postal_code</td>\n",
       "      <td>0.088172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>longitude</td>\n",
       "      <td>0.080466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bedrooms_per_room</td>\n",
       "      <td>0.070059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>latitude</td>\n",
       "      <td>0.066439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rooms_per_household</td>\n",
       "      <td>0.048305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>housing_median_age</td>\n",
       "      <td>0.030146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>city</td>\n",
       "      <td>0.022991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Column  Importance\n",
       "7              median_income    0.248165\n",
       "14    ocean_proximity_INLAND    0.135266\n",
       "13  population_per_household    0.088856\n",
       "9                postal_code    0.088172\n",
       "0                  longitude    0.080466\n",
       "12         bedrooms_per_room    0.070059\n",
       "1                   latitude    0.066439\n",
       "10       rooms_per_household    0.048305\n",
       "2         housing_median_age    0.030146\n",
       "8                       city    0.022991"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# expected shape - (n_features, 2)\n",
    "feature_importance = rf_feature_importance(model, X)\n",
    "\n",
    "# peek at top 10 features\n",
    "feature_importance[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table we see that `median_income`, `ocean_proximity_INLAND`, and `population_per_household` are the most important features - this is not entirely surprising since income and house location seem to be good indicators of house value. We can also plot the feature importance to gain a visual understanding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance(feature_importance):\n",
    "    return sns.barplot(y=\"Column\", x=\"Importance\", data=feature_importance, color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(feature_importance);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In nearly every real-world dataset, this is what the feature importance looks like: a handful of columns are very important, while most are not. The powerful aspect of this approach is that is _focuses our attention_ on which features we should investigate further and which ones we can safely ignore."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Warning: The feature importance analysis above can be biased and has a tendency to inflate the importance of continuous features or  categorical features with high cardinality (i.e. many unique categories). See the Beware Default Random Forest Importances article in the references for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop uninformative features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the feature importance plot above, we can see there are only a handful of informative features - let's use this insight to make a simpler model by dropping uninformative columns from our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_threshold = 0.03\n",
    "cols_to_keep = feature_importance[\n",
    "    feature_importance['Importance'] > feature_importance_threshold\n",
    "]['Column']\n",
    "\n",
    "len(cols_to_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a copy of the data with selected columns and create new train / test set\n",
    "X_keep = X.copy()[cols_to_keep]\n",
    "X_train, X_valid = train_test_split(X_keep, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity_INLAND</th>\n",
       "      <th>population_per_household</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>longitude</th>\n",
       "      <th>bedrooms_per_room</th>\n",
       "      <th>latitude</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>housing_median_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>94705</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>0.146591</td>\n",
       "      <td>37.88</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>94611</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>0.155797</td>\n",
       "      <td>37.86</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>94618</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>0.129516</td>\n",
       "      <td>37.85</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>94618</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>0.184458</td>\n",
       "      <td>37.85</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>94618</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>0.172096</td>\n",
       "      <td>37.85</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   median_income  ocean_proximity_INLAND  population_per_household  \\\n",
       "0         8.3252                       0                  2.555556   \n",
       "1         8.3014                       0                  2.109842   \n",
       "2         7.2574                       0                  2.802260   \n",
       "3         5.6431                       0                  2.547945   \n",
       "4         3.8462                       0                  2.181467   \n",
       "\n",
       "   postal_code  longitude  bedrooms_per_room  latitude  rooms_per_household  \\\n",
       "0        94705    -122.23           0.146591     37.88             6.984127   \n",
       "1        94611    -122.22           0.155797     37.86             6.238137   \n",
       "2        94618    -122.24           0.129516     37.85             8.288136   \n",
       "3        94618    -122.25           0.184458     37.85             5.817352   \n",
       "4        94618    -122.25           0.172096     37.85             6.281853   \n",
       "\n",
       "   housing_median_age  \n",
       "0                41.0  \n",
       "1                21.0  \n",
       "2                52.0  \n",
       "3                52.0  \n",
       "4                52.0  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_keep.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, let's ensure the model's prediction have not gotten worse with the reduced data (recall we had $R^2 = 0.91$ on the validation set):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on train: 16622.874\n",
      "R^2 on train: 0.97\n",
      "RMSE on valid: 43873.09\n",
      "R^2 on valid: 0.8\n",
      "OOB R^2: 0.789\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(\n",
    "    n_estimators=40, min_samples_leaf=1, n_jobs=-1, oob_score=True, random_state=42\n",
    ")\n",
    "model.fit(X_train, y_train)\n",
    "print_rf_scores(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X_keep)\n",
    "plot_feature_importance(feature_importance);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've now got a model that isn't really more predictive than our baseline, but it's much simpler - it has just 9 features instead of 19 and we now know that `median_income` and `ocean_proximity_INLAND` are particularly important features to focus on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Exercise #2\n",
    "\n",
    "Go through the top 3 features and use plots with `seaborn` to gain insights into things like the following:\n",
    "\n",
    "* What is the relationship between each feature and the target `median_house_value`?\n",
    "* What do the distribution / bar plots look like for continuous / categorical columns?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing redundant features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at our feature importance plots, we can see that there are some features that seem to be related to each other (e.g. the square footage features like `rooms_per_household` and `bedrooms_per_room`). Features like this are potentially measuring the same thing, so we can use a special technique called _**hierarchical clustering**_ to produce something called a _**dendogram**_ that will tell us which pairs of features are similar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_dendogram(X):\n",
    "    \"\"\"Plots a dendogram to see which features are related.\"\"\"\n",
    "    # calculate correlation coefficient\n",
    "    corr = np.round(scipy.stats.spearmanr(X).correlation, 4)\n",
    "    # convert to distance matrix\n",
    "    corr_condensed = hc.distance.squareform(1 - corr)\n",
    "    # perform clustering\n",
    "    z = hc.linkage(corr_condensed, method=\"average\")\n",
    "    # plot dendogram\n",
    "    fig = plt.figure(figsize=(16, 10))\n",
    "    dendrogram = hc.dendrogram(\n",
    "        z, labels=X.columns, orientation=\"left\", leaf_font_size=16\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dendogram(X_keep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot we see that quantities like `latitude` and `postal_code` are grouped together and similar (as we might expect). Note that we used Spearman's rank correlation coefficient to calculate notions of similarity - this is useful for finding non-linear correlations that may be missed by Pearson's correlation coefficient.\n",
    "\n",
    "To examine these correlations a bit deeper, let's create a function that trains a Random Forest on subsets of the data where one of the columns is removed and see in which cases the OOB score does not get worse:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_oob(df):\n",
    "    model = RandomForestRegressor(\n",
    "        n_estimators=40, max_features='sqrt', n_jobs=-1, oob_score=True, random_state=42\n",
    "    )\n",
    "    X, _ = train_test_split(df, test_size=0.2, random_state=42)\n",
    "    model.fit(X, y_train)\n",
    "    return model.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8021265907435767"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate reference value\n",
    "get_oob(X_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "longitude 0.7728875237498377\n",
      "population_per_household 0.7901969073954147\n",
      "housing_median_age 0.7921744780471269\n",
      "bedrooms_per_room 0.8033772050439794\n",
      "latitude 0.7764470270681797\n",
      "postal_code 0.7817819559028422\n",
      "rooms_per_household 0.8039031326542978\n",
      "median_income 0.7947438916177811\n"
     ]
    }
   ],
   "source": [
    "for column in (\n",
    "    \"longitude\",\n",
    "    \"population_per_household\",\n",
    "    \"housing_median_age\",\n",
    "    \"bedrooms_per_room\",\n",
    "    \"latitude\",\n",
    "    \"postal_code\",\n",
    "    \"rooms_per_household\",\n",
    "    \"median_income\",\n",
    "):\n",
    "    print(column, get_oob(X_keep.drop(column, axis=1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we're looking for columns where the OOB score did not drop much, say around the third decimal place. Let's see what happens to our OOB score when we drop these candidate columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.805703437136301"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_drop = [\n",
    "    'bedrooms_per_room',\n",
    "    \"rooms_per_household\",\n",
    "]\n",
    "get_oob(X_keep.drop(cols_to_drop, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The OOB score increases fractionally which is good since we're looking for a simpler model - let's drop these columns and run the full model again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_keep.drop(cols_to_drop, axis=1, inplace=True)\n",
    "X_train, X_valid = train_test_split(X_keep, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on train: 15876.383\n",
      "R^2 on train: 0.973\n",
      "RMSE on valid: 41855.649\n",
      "R^2 on valid: 0.818\n",
      "OOB R^2: 0.806\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(n_estimators=40, max_features='sqrt', n_jobs=-1, oob_score=True, random_state=42)\n",
    "model.fit(X_train, y_train)\n",
    "print_rf_scores(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X_keep)\n",
    "plot_feature_importance(feature_importance);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our final model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have simplified our model, let's create a huge Random Forest to see if we can squeeze a bit more performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on train: 14963.277\n",
      "R^2 on train: 0.976\n",
      "RMSE on valid: 41200.693\n",
      "R^2 on valid: 0.823\n",
      "OOB R^2: 0.823\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor(\n",
    "    n_estimators=1000, max_features='sqrt', n_jobs=-1, oob_score=True, random_state=42\n",
    ")\n",
    "model.fit(X_train, y_train)\n",
    "print_rf_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our final model achieves around 5% better performance compared to our naive model with 10 trees from lesson 4, but it is _much simpler_ - we've reduced the features from 19 down to 7! Moreover we've identified which features are most important, let's plot them out for the final visual comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X_keep)\n",
    "plot_feature_importance(feature_importance);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model to disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let's save this model to disk for later use or if we want to deploy it to production:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(DATA/'housing_model.pkl', mode='wb') as file:\n",
    "    pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sanity check\n",
    "with open(DATA/'housing_model.pkl', mode='rb') as file:\n",
    "    model = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on train: 14963.277\n",
      "R^2 on train: 0.976\n",
      "RMSE on valid: 41200.693\n",
      "R^2 on valid: 0.823\n",
      "OOB R^2: 0.823\n"
     ]
    }
   ],
   "source": [
    "print_rf_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: We often use the `with` statement to read or write data in Python as this automatically takes care of closing the file once we have finished with it. The `mode` argument controls how we want to open the file, where `'rb'` (read) and `'wb'` (write) correspond to opening in binary mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Exercise #3\n",
    "\n",
    "By this stage you've now learned all the key steps needed to clean data, train a machine learning model, and interpret the results! Try applying the techniques you have learned to Kaggle's [House Prices: Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques) competition. Don't spend too much time trying to build a perfect model - the purpose of this exercise is to get familiar with downloading a new dataset, understanding the evaluation metric, and submitting your predictions to the Kaggle platform. To get started:\n",
    "\n",
    "* Create an account on [Kaggle](https://www.kaggle.com/)\n",
    "* Download the data from [here](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)\n",
    "\n",
    "When you have your predictions ready, submit them [here](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/submissions) for evaluation on the public leaderboard!\n",
    "\n",
    "Hint #1: Kaggle provides 2 datasets called `train.csv` and `test.csv`, where the latter is missing the target column `SalePrice`. Your task is to build a regression model on `train.csv` and then use that model to make _predictions_ on `test.csv`. \n",
    "\n",
    "Hint #2: A closer look at Kaggle's evaluation metric shows that we actually want the RMSE between the _logarithms_ of the predicted and actual house prices:\n",
    "\n",
    "> Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)\n",
    "\n",
    "How might you handle this? You may find NumPy's [log function](https://docs.scipy.org/doc/numpy/reference/generated/numpy.log.html) to be useful here.\n",
    "\n",
    "Hint #3: You may find the `convert_strings_to_categories` function from lesson 3 to be useful. You can import it from the `dslectures` library as follows\n",
    "\n",
    "```python\n",
    "from dslectures.core import convert_strings_to_categories\n",
    "```\n",
    "\n",
    "Hint #4: When you use the median to fill missing values in the training set, you should use the _same_ median for the test set. See p.61 of _Hands-On Machine Learning with Scikit-Learn and TensorFlow_ by A. Geron.\n",
    "\n",
    "---\n"
   ]
  }
 ],
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