lectures/12_feature-selection-and-review.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# CPSC 330 Lecture 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lecture plan\n",
    "\n",
    "- 👋\n",
    "- **Turn on recording**\n",
    "- Announcements (5 min)\n",
    "- Lecture 11 True/False questions (10 min)\n",
    "- Feature correlations (10 min)\n",
    "- Feature selection: intro (5 min)\n",
    "- Feature selection: importance-based feature elimination (15 min)\n",
    "- Break (5 min)\n",
    "- Midterm review (30 min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_validate, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler, OrdinalEncoder, OneHotEncoder\n",
    "from sklearn.linear_model import Ridge, RidgeCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.dummy import DummyRegressor\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.compose import ColumnTransformer, TransformedTargetRegressor\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.feature_selection import RFE, RFECV\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import cross_validate_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.size'] = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Announcements\n",
    "\n",
    "- hw5 deadline passed \n",
    "  - hw6 posted in 1-2 weeks, due in 3 weeks\n",
    "- Learning objectives and summaries now available for Lectures 2-11\n",
    "- Midterm this Thursday in class!\n",
    "- Midterm instructions: https://piazza.com/class/kb2e6nwu3uj23?cid=376"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspirational speech on academic integrity (2 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lecture 11 T/F (10 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data processing code from last class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/housing.csv\", index_col=0)\n",
    "\n",
    "df_train, df_test = train_test_split(df, random_state=123)\n",
    "\n",
    "X_train = df_train.drop(columns=['SalePrice'])\n",
    "y_train = df_train['SalePrice']\n",
    "\n",
    "X_test = df_test.drop(columns=['SalePrice'])\n",
    "y_test = df_test['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features     = ['LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', \n",
    "                        'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', \n",
    "                        'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', \n",
    "                        'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', \n",
    "                        'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', \n",
    "                        'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', \n",
    "                        'ScreenPorch', 'PoolArea', 'MiscVal', 'YrSold']\n",
    "ordinal_features_reg = ['ExterQual', 'ExterCond', 'BsmtQual', 'BsmtCond', 'HeatingQC', \n",
    "                        'KitchenQual', 'FireplaceQu', 'GarageQual', 'GarageCond', 'PoolQC']\n",
    "ordinal_features_oth = ['BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', \n",
    "                        'Functional',  'Fence']\n",
    "categorical_features = list(set(X_train.columns) - set(numeric_features) - set(ordinal_features_reg))\n",
    "\n",
    "ordering = ['Po', 'Fa', 'TA', 'Gd', 'Ex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_preprocessing = make_pipeline(SimpleImputer(strategy='median'), \n",
    "                                      StandardScaler())\n",
    "ordinal_preprocessing = make_pipeline(SimpleImputer(strategy='most_frequent'), \n",
    "                                      OrdinalEncoder(categories=[ordering]*len(ordinal_features_reg)))\n",
    "categorical_preprocessing = make_pipeline(SimpleImputer(strategy='constant', fill_value=\"?\"),\n",
    "                                          OneHotEncoder(handle_unknown='ignore', sparse=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessing = ColumnTransformer([\n",
    "    ('numeric', numeric_preprocessing, numeric_features),\n",
    "    ('ordinal', ordinal_preprocessing, ordinal_features_reg),\n",
    "    ('categorical', categorical_preprocessing, categorical_features)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessing.fit(X_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_columns = list(preprocessing.named_transformers_['categorical'].named_steps['onehotencoder'].get_feature_names(categorical_features))\n",
    "new_columns = numeric_features + ordinal_features_reg + ohe_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
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      "text/plain": [
       "      LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "Id                                                                 \n",
       "1447    -0.046315  1.654400    -0.775646     1.255836  -0.282035   \n",
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       "187     -0.046315 -0.036795     0.647021    -0.508295   0.634466   \n",
       "1021    -0.420591 -0.342035    -1.486980    -0.508295   1.125448   \n",
       "68       0.140824  0.038184     0.647021    -0.508295   1.059984   \n",
       "...           ...       ...          ...          ...        ...   \n",
       "1042    -0.046315 -0.122111    -0.064312     2.137902  -0.151106   \n",
       "1123    -0.046315 -0.143414    -1.486980    -2.272427  -0.478428   \n",
       "1347    -0.046315  1.094567     0.647021     1.255836  -0.085642   \n",
       "1407     0.047255 -0.193644    -0.775646     1.255836   0.045287   \n",
       "1390    -0.420591 -0.448968    -0.064312     0.373770  -0.969410   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  ...  \\\n",
       "Id                                                                 ...   \n",
       "1447     -1.105566    0.491436    0.327430   -0.284980   0.064219  ...   \n",
       "1124      1.117128   -0.569906   -0.942714   -0.284980   0.297501  ...   \n",
       "187       0.295697   -0.569906    0.365985   -0.284980   0.023451  ...   \n",
       "1021      0.972169   -0.569906    1.250588   -0.284980  -1.038776  ...   \n",
       "68        0.875531    0.367894    1.227027   -0.284980  -0.286837  ...   \n",
       "...            ...         ...         ...         ...        ...  ...   \n",
       "1042      0.730572    0.845217   -0.085956    0.100152  -0.522384  ...   \n",
       "1123     -1.395483   -0.569906   -0.942714   -0.284980   0.238615  ...   \n",
       "1347      0.875531   -0.569906   -0.306571    0.124223   1.441264  ...   \n",
       "1407      1.068808   -0.569906    0.462370   -0.284980  -1.029716  ...   \n",
       "1390     -1.685400   -0.569906   -0.139503   -0.284980  -0.468027  ...   \n",
       "\n",
       "      Exterior1st_Wd Sdng  Exterior1st_WdShing  BedroomAbvGr_0  \\\n",
       "Id                                                               \n",
       "1447                  0.0                  0.0             0.0   \n",
       "1124                  0.0                  0.0             0.0   \n",
       "187                   0.0                  0.0             0.0   \n",
       "1021                  0.0                  0.0             0.0   \n",
       "68                    0.0                  0.0             0.0   \n",
       "...                   ...                  ...             ...   \n",
       "1042                  0.0                  0.0             0.0   \n",
       "1123                  0.0                  0.0             0.0   \n",
       "1347                  0.0                  0.0             0.0   \n",
       "1407                  0.0                  0.0             0.0   \n",
       "1390                  1.0                  0.0             0.0   \n",
       "\n",
       "      BedroomAbvGr_1  BedroomAbvGr_2  BedroomAbvGr_3  BedroomAbvGr_4  \\\n",
       "Id                                                                     \n",
       "1447             0.0             0.0             1.0             0.0   \n",
       "1124             0.0             1.0             0.0             0.0   \n",
       "187              0.0             0.0             1.0             0.0   \n",
       "1021             0.0             1.0             0.0             0.0   \n",
       "68               0.0             0.0             1.0             0.0   \n",
       "...              ...             ...             ...             ...   \n",
       "1042             0.0             0.0             0.0             1.0   \n",
       "1123             0.0             0.0             1.0             0.0   \n",
       "1347             0.0             0.0             1.0             0.0   \n",
       "1407             0.0             1.0             0.0             0.0   \n",
       "1390             0.0             0.0             1.0             0.0   \n",
       "\n",
       "      BedroomAbvGr_5  BedroomAbvGr_6  BedroomAbvGr_8  \n",
       "Id                                                    \n",
       "1447             0.0             0.0             0.0  \n",
       "1124             0.0             0.0             0.0  \n",
       "187              0.0             0.0             0.0  \n",
       "1021             0.0             0.0             0.0  \n",
       "68               0.0             0.0             0.0  \n",
       "...              ...             ...             ...  \n",
       "1042             0.0             0.0             0.0  \n",
       "1123             0.0             0.0             0.0  \n",
       "1347             0.0             0.0             0.0  \n",
       "1407             0.0             0.0             0.0  \n",
       "1390             0.0             0.0             0.0  \n",
       "\n",
       "[1095 rows x 292 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_enc = pd.DataFrame(preprocessing.transform(X_train), index=X_train.index, columns=new_columns)\n",
    "X_train_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_enc = pd.DataFrame(preprocessing.transform(X_test), index=X_test.index, columns=new_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature correlations (10 min)\n",
    "\n",
    "(This should probably have gone in the feature importances lecture, L10.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We'll look at the correlations between various features with other features and the target (first row/column). \n",
    "- What is correlation, in simple terms:\n",
    "  - If $Y$ goes up when $X$ goes up, we say $X$ and $Y$ are positively correlated.\n",
    "  - If $Y$ goes down when $X$ goes up, we say $X$ and $Y$ are negatively correlated.\n",
    "  - If $Y$ is unchanged when $X$ changes, we say $X$ and $Y$ are uncorrelated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "cor = pd.concat((y_train, X_train_enc),axis=1).iloc[:,:15].corr();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "sns.set(font_scale=1)\n",
    "sns.heatmap(cor, annot=True, cmap=plt.cm.Blues);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: we got similar diagrams from `pandas_profiler`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can immediately see that `SalePrice` is highly correlated with `OverallQual`.\n",
    "- This is an early hint that `OverallQual` is a useful feature in predicting `SalePrice`.\n",
    "- However, this approach is **extremely simplistic**.\n",
    "  - It only looks at each feature in isolation.\n",
    "  - It only looks at linear associations:\n",
    "    - What if `SalePrice` is high when `BsmtFullBath` is 2 or 3, but low when it's 0, 1, or 4? They might seem uncorrelated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looking at this diagram also tells us the relationship between features. \n",
    "  - For example, `1stFlrSF` and `TotalBsmtSF` are highly correlated. \n",
    "  - Do we need both of them?\n",
    "  - If our model says `1stFlrSF` is very important and `TotalBsmtSF` is very unimportant, do we trust those values?\n",
    "  - Maybe `TotalBsmtSF` only \"becomes important\" if `1stFlrSF` is removed.\n",
    "  - Sometimes the opposite happens: a feature only becomes important if another feature is _added_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Feature selection intro (5 min)\n",
    "\n",
    "- What is feature selection?\n",
    "- Find the features\t(columns) $X$ that are important for predicting\t$y$, and remove the features that aren't. \n",
    "- Very related to looking at feature importances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which of these features are useful?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why we might want to ask this? Why not just use all of them, even the fairly unimportant ones?\n",
    "\n",
    "- Data collection: What type of new data should I collect? It may be cheaper to collect fewer columns.\n",
    "- Computation: Models fit/predict faster with fewer columns.\n",
    "- Fundamental tradeoff: Can I reduce overfitting by removing useless features?\n",
    "  - Think about the random noise column in hw5.\n",
    "  - What does it do to training error?\n",
    "  - What does it do to validation error?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to do feature selection? The main approaches I will recommend in this course:\n",
    "\n",
    "1. Domain knowledge\n",
    "2. Importance-based feature elimination\n",
    "\n",
    "Removed from the course: lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Feature selection: importance-based feature elimination (15 min)\n",
    "\n",
    "- We already had a whole lecture on feature importances.\n",
    "- Let's use these importances to eliminate unimportant features (i.e., those with low importance).\n",
    "- Basic idea: throw away the least important feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Recursive feature elimination \n",
    "\n",
    "1. Decide $k$, the number of features to select. \n",
    "2. Assign importances to features, e.g. by fitting a model and looking at `coef_` or `feature_importances_`.\n",
    "3. Remove the least important feature.\n",
    "4. Repeat steps 2-3 until only $k$ features are remaining.\n",
    "\n",
    "Note that this is **not** the same as just removing all the less important features in one shot!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In scikit-learn, we'll do this using `RFE`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's look at the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html).\n",
    "\n",
    "- **Note**: `RFE` not currently set up to work well with `Pipeline` or `TransformedTargetRegressor` !!\n",
    "- See https://github.com/scikit-learn/scikit-learn/issues/15154, https://github.com/scikit-learn/scikit-learn/issues/9606, https://github.com/scikit-learn/scikit-learn/issues/15312"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_log = np.log(y_train)\n",
    "y_test_log = np.log(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "292"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_enc.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "        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>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>...</th>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <th>BedroomAbvGr_0</th>\n",
       "      <th>BedroomAbvGr_1</th>\n",
       "      <th>BedroomAbvGr_2</th>\n",
       "      <th>BedroomAbvGr_3</th>\n",
       "      <th>BedroomAbvGr_4</th>\n",
       "      <th>BedroomAbvGr_5</th>\n",
       "      <th>BedroomAbvGr_6</th>\n",
       "      <th>BedroomAbvGr_8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1447</th>\n",
       "      <td>-0.046315</td>\n",
       "      <td>1.654400</td>\n",
       "      <td>-0.775646</td>\n",
       "      <td>1.255836</td>\n",
       "      <td>-0.282035</td>\n",
       "      <td>-1.105566</td>\n",
       "      <td>0.491436</td>\n",
       "      <td>0.327430</td>\n",
       "      <td>-0.28498</td>\n",
       "      <td>0.064219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>-0.888437</td>\n",
       "      <td>-0.093394</td>\n",
       "      <td>-0.775646</td>\n",
       "      <td>3.019968</td>\n",
       "      <td>-0.773017</td>\n",
       "      <td>1.117128</td>\n",
       "      <td>-0.569906</td>\n",
       "      <td>-0.942714</td>\n",
       "      <td>-0.28498</td>\n",
       "      <td>0.297501</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>-0.046315</td>\n",
       "      <td>-0.036795</td>\n",
       "      <td>0.647021</td>\n",
       "      <td>-0.508295</td>\n",
       "      <td>0.634466</td>\n",
       "      <td>0.295697</td>\n",
       "      <td>-0.569906</td>\n",
       "      <td>0.365985</td>\n",
       "      <td>-0.28498</td>\n",
       "      <td>0.023451</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>-0.420591</td>\n",
       "      <td>-0.342035</td>\n",
       "      <td>-1.486980</td>\n",
       "      <td>-0.508295</td>\n",
       "      <td>1.125448</td>\n",
       "      <td>0.972169</td>\n",
       "      <td>-0.569906</td>\n",
       "      <td>1.250588</td>\n",
       "      <td>-0.28498</td>\n",
       "      <td>-1.038776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.140824</td>\n",
       "      <td>0.038184</td>\n",
       "      <td>0.647021</td>\n",
       "      <td>-0.508295</td>\n",
       "      <td>1.059984</td>\n",
       "      <td>0.875531</td>\n",
       "      <td>0.367894</td>\n",
       "      <td>1.227027</td>\n",
       "      <td>-0.28498</td>\n",
       "      <td>-0.286837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 292 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "Id                                                                 \n",
       "1447    -0.046315  1.654400    -0.775646     1.255836  -0.282035   \n",
       "1124    -0.888437 -0.093394    -0.775646     3.019968  -0.773017   \n",
       "187     -0.046315 -0.036795     0.647021    -0.508295   0.634466   \n",
       "1021    -0.420591 -0.342035    -1.486980    -0.508295   1.125448   \n",
       "68       0.140824  0.038184     0.647021    -0.508295   1.059984   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  ...  \\\n",
       "Id                                                                 ...   \n",
       "1447     -1.105566    0.491436    0.327430    -0.28498   0.064219  ...   \n",
       "1124      1.117128   -0.569906   -0.942714    -0.28498   0.297501  ...   \n",
       "187       0.295697   -0.569906    0.365985    -0.28498   0.023451  ...   \n",
       "1021      0.972169   -0.569906    1.250588    -0.28498  -1.038776  ...   \n",
       "68        0.875531    0.367894    1.227027    -0.28498  -0.286837  ...   \n",
       "\n",
       "      Exterior1st_Wd Sdng  Exterior1st_WdShing  BedroomAbvGr_0  \\\n",
       "Id                                                               \n",
       "1447                  0.0                  0.0             0.0   \n",
       "1124                  0.0                  0.0             0.0   \n",
       "187                   0.0                  0.0             0.0   \n",
       "1021                  0.0                  0.0             0.0   \n",
       "68                    0.0                  0.0             0.0   \n",
       "\n",
       "      BedroomAbvGr_1  BedroomAbvGr_2  BedroomAbvGr_3  BedroomAbvGr_4  \\\n",
       "Id                                                                     \n",
       "1447             0.0             0.0             1.0             0.0   \n",
       "1124             0.0             1.0             0.0             0.0   \n",
       "187              0.0             0.0             1.0             0.0   \n",
       "1021             0.0             1.0             0.0             0.0   \n",
       "68               0.0             0.0             1.0             0.0   \n",
       "\n",
       "      BedroomAbvGr_5  BedroomAbvGr_6  BedroomAbvGr_8  \n",
       "Id                                                    \n",
       "1447             0.0             0.0             0.0  \n",
       "1124             0.0             0.0             0.0  \n",
       "187              0.0             0.0             0.0  \n",
       "1021             0.0             0.0             0.0  \n",
       "68               0.0             0.0             0.0  \n",
       "\n",
       "[5 rows x 292 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_enc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = Ridge(alpha=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fit_time           0.010989\n",
       "score_time         0.005164\n",
       "test_score         0.856698\n",
       "train_score        0.898979\n",
       "std_test_score     0.096162\n",
       "std_train_score    0.005990\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate_std(lr, X_train_enc, y_train_log, return_train_score=True, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(X_train_enc, y_train_log);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8981098599473102"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_train_enc, y_train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9051318521975947"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test_enc, y_test_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 292)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_enc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.079477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>0.044766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.043596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>0.040942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>0.035982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <td>0.000075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold_9</th>\n",
       "      <td>0.000022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Coefficients\n",
       "OverallQual             0.079477\n",
       "GarageCars              0.044766\n",
       "GrLivArea               0.043596\n",
       "OverallCond             0.040942\n",
       "BsmtQual                0.035982\n",
       "...                          ...\n",
       "Exterior1st_CBlock      0.000075\n",
       "Exterior2nd_CBlock      0.000075\n",
       "SaleType_ConLI          0.000064\n",
       "Exterior1st_Stone       0.000058\n",
       "MoSold_9                0.000022\n",
       "\n",
       "[292 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df = pd.DataFrame(data=np.abs(lr.coef_), index=X_train_enc.columns, columns=[\"Coefficients\"]).sort_values(by=\"Coefficients\", ascending=False)\n",
    "coef_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the top 30 features, one at a time, based on the absolute value of `model.coef_`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe = RFE(lr, n_features_to_select=30)\n",
    "rfe.fit(X_train_enc, y_train_log);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.876925559161639"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.score(X_train_enc, y_train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8823400488225399"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.score(X_test_enc, y_test_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OverallQual', 'OverallCond', 'YearBuilt', 'TotalBsmtSF', '1stFlrSF',\n",
       "       '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageCars', 'ExterQual', 'BsmtQual', 'Foundation_PConc',\n",
       "       'BsmtExposure_Gd', 'MSSubClass_30', 'MSSubClass_160',\n",
       "       'Neighborhood_Crawfor', 'Neighborhood_NridgHt', 'Neighborhood_Somerst',\n",
       "       'GarageType_Attchd', 'BsmtFinType1_Unf', 'CentralAir_N', 'CentralAir_Y',\n",
       "       'BldgType_1Fam', 'MSZoning_C (all)', 'MSZoning_RL', 'MSZoning_RM',\n",
       "       'SaleCondition_Abnorml', 'Condition1_Norm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = X_train_enc.columns[rfe.support_]\n",
    "selected_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contrast with the top 30 by the ORIGINAL importances (absolute value coefficients):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['OverallQual', 'GarageCars', 'GrLivArea', 'OverallCond', 'BsmtQual',\n",
       "       'Neighborhood_Crawfor', 'Condition1_Norm', 'MSZoning_C (all)',\n",
       "       '1stFlrSF', 'Neighborhood_NridgHt', 'MSZoning_RL', 'TotRmsAbvGrd',\n",
       "       'Fireplaces', '2ndFlrSF', 'BldgType_1Fam', 'YearBuilt', 'MSSubClass_20',\n",
       "       'Neighborhood_Somerst', 'Foundation_PConc', 'FullBath',\n",
       "       'Neighborhood_Edwards', 'MSSubClass_30', 'BsmtFullBath',\n",
       "       'BsmtFinType1_GLQ', 'MSSubClass_160', 'Exterior1st_BrkFace',\n",
       "       'CentralAir_Y', 'CentralAir_N', 'SaleCondition_Abnorml',\n",
       "       'BsmtFinType1_Unf'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_df.iloc[:30].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BsmtExposure_Gd',\n",
       " 'ExterQual',\n",
       " 'GarageType_Attchd',\n",
       " 'MSZoning_RM',\n",
       " 'TotalBsmtSF'}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(selected_columns.values) - set(coef_df.iloc[:30].index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([129,  44,   1,   1,   1,  46, 193, 120, 104, 121,   1,   1,   1,\n",
       "       195,   1,   1, 138,  19,  20,   1,   1,  91,   1,  58,  25, 250,\n",
       "       161, 102,  26,  59, 205,  69,   1, 151,   1, 143,  11,   4,  53,\n",
       "        23,  39,  96,  68, 119,   1, 111, 256, 196, 181,  55, 148, 134,\n",
       "       157, 204, 171, 172,  93, 170, 162, 201, 126, 133, 144, 208, 251,\n",
       "        70, 145, 159, 160, 261, 241,  12, 210,  33,  38, 247,   1, 232,\n",
       "        36,   7,   1, 207, 248,  54, 178,  40, 253, 142, 214,  92, 123,\n",
       "         1,  89, 209,  29, 168,  60,  15, 149, 112, 175,  74, 113,  75,\n",
       "       114, 263,  27, 180, 203,   5,  48, 231, 192, 221, 236, 176, 259,\n",
       "       110, 234, 100, 130, 244, 122, 229,  52, 154, 198,  85,  98, 217,\n",
       "        95,  64,  66,  81,   1,   3,  45,  22,  21,  79, 197, 165,  86,\n",
       "        35,   1,  34, 255,  77,  94,   1,  14, 135, 109, 163, 139,   1,\n",
       "       153, 179, 141,  90, 202, 125,  73, 199, 140, 156, 106, 164,  50,\n",
       "       185,  61, 230, 200, 184, 103, 228, 186, 222, 215, 218, 245,   6,\n",
       "        17, 174, 252, 211, 213,  57, 183,  49, 240, 254, 233, 150, 116,\n",
       "       117,  62, 127, 239, 158, 235,  56, 128, 242,  16, 226, 155,   1,\n",
       "       137, 189, 147, 194, 216,  84, 105, 169,   1,   1,  13,  87, 225,\n",
       "       258, 257, 227,  99,  71,  63,  43, 166,  18,  41, 191, 188, 132,\n",
       "        65, 177, 212, 220,  42,   1, 167,  83,   8, 124,   1,  24, 118,\n",
       "         1,   1,   1, 246, 190, 115,  32,  31, 224,   9, 108, 243, 107,\n",
       "        51,  30,   1, 146,  88, 131, 152, 223, 238,  78,  80, 206, 249,\n",
       "         2, 260,  82, 101,  47, 187, 262,  28,  67,  37, 182, 136,  76,\n",
       "       219,  97,  10,  72, 237, 173])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.ranking_ # may be different than the ranked feature importances!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do this same thing with a random forest. `RFE` will automatically use the random forests's `feature_importances_` instead of linear regression's `coef_`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8891910313672117"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(random_state=123, max_depth=5, n_estimators=10)\n",
    "rf.fit(X_train_enc, y_train_log);\n",
    "rf.score(X_train_enc, y_train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.819405420056765"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.score(X_test_enc, y_test_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfe = RFE(rf, n_features_to_select=100)\n",
    "rfe.fit(X_train_enc, y_train_log);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8900923726684768"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.score(X_train_enc, y_train_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8204816375480437"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe.score(X_test_enc, y_test_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO\n",
    "\n",
    "plot train and test error (or really train and valid error) vs. number of feature selected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In this case the model was overfitting and feature selection helped a tiny bit.\n",
    "- But we may have achieved the same by tuning hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about categorical features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1',\n",
       "       'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath',\n",
       "       'FullBath', 'HalfBath', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF',\n",
       "       'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch',\n",
       "       'PoolArea', 'YrSold', 'ExterQual', 'ExterCond', 'BsmtQual',\n",
       "       'HeatingQC', 'KitchenQual', 'PoolQC', 'RoofStyle_Hip',\n",
       "       'Fence_GdWo', 'BsmtExposure_No', 'MSSubClass_20', 'MSSubClass_30',\n",
       "       'MSSubClass_60', 'MoSold_10', 'MoSold_12', 'LandSlope_Gtl',\n",
       "       'Exterior2nd_AsbShng', 'Exterior2nd_AsphShn',\n",
       "       'Exterior2nd_Brk Cmn', 'Exterior2nd_BrkFace',\n",
       "       'Neighborhood_Crawfor', 'Neighborhood_Gilbert',\n",
       "       'GarageType_Detchd', 'Alley_?', 'Alley_Grvl', 'Alley_Pave',\n",
       "       'MasVnrType_?', 'MasVnrType_BrkCmn', 'MasVnrType_BrkFace',\n",
       "       'MasVnrType_None', 'MasVnrType_Stone', 'BsmtFinType2_ALQ',\n",
       "       'BsmtFinType2_LwQ', 'BsmtFinType2_Rec', 'Electrical_FuseF',\n",
       "       'Electrical_FuseP', 'LandContour_HLS', 'GarageFinish_Fin',\n",
       "       'BsmtFinType1_BLQ', 'BsmtFinType1_Unf', 'HouseStyle_1.5Fin',\n",
       "       'CentralAir_N', 'CentralAir_Y', 'KitchenAbvGr_2', 'Functional_Typ',\n",
       "       'MSZoning_C (all)', 'MSZoning_RM', 'SaleCondition_Family',\n",
       "       'SaleCondition_Normal', 'LotConfig_Inside', 'Condition1_Feedr',\n",
       "       'Condition1_Norm', 'Condition1_RRAe', 'Condition1_RRAn',\n",
       "       'Condition1_RRNe', 'Condition1_RRNn', 'Street_Grvl', 'Street_Pave',\n",
       "       'Exterior1st_AsbShng', 'Exterior1st_BrkComm',\n",
       "       'Exterior1st_BrkFace', 'Exterior1st_CBlock', 'Exterior1st_CemntBd',\n",
       "       'Exterior1st_HdBoard', 'Exterior1st_MetalSd',\n",
       "       'Exterior1st_Plywood', 'Exterior1st_Stone', 'Exterior1st_Stucco',\n",
       "       'Exterior1st_VinylSd', 'Exterior1st_Wd Sdng'], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = X_train_enc.columns[rfe.support_]\n",
    "selected_columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_columns = [col for col in X_train_enc.columns if col not in selected_columns]\n",
    "removed_columns;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Does it make sense that we removed `BldgType_Duplex` but kept `BldgType_1Fam`?\n",
    "  - This is a matter of opinion, I guess.\n",
    "- There are more sophisticated ways of selecting categorical features.\n",
    "- This seems to be unresolved in sklearn: https://github.com/scikit-learn/scikit-learn/issues/8480"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### How to choose the number of features to select?\n",
    "\n",
    "- Suppose you don't know the number of relevant features in advance, \n",
    "- You can find them using cross validation with [RFECV](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV)\n",
    "  - You would definitely not want to use training error! (Why?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of selected features: 60/292\n"
     ]
    }
   ],
   "source": [
    "lr = Ridge(alpha=100)\n",
    "\n",
    "rfe_cv = RFECV(lr)\n",
    "rfe_cv.fit(X_train_enc, y_train_log)\n",
    "print('Number of selected features: %d/%d' % (rfe_cv.n_features_, X_train_enc.shape[1]))\n",
    "# print('Feature mask:', rfe_cv.support_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It stopped once it got to 60 features.\n",
    "- Dropping down to 59 would lower the cross-validation score.\n",
    "  - This is similar to using cross-validation to tune a hyperparameter.\n",
    "  - Indeed, the number of selected features _is_ a hyperparameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: We are violating the Golden Rule a bit here, see above for sklearn issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Warnings about feature selection \n",
    "\n",
    "- A feature's relevance is only defined in the context of other features.\n",
    "    - Adding/removing features can make features relevant/irrelevant.\n",
    "- If features can be predicted from other other features, you cannot know which one to pick. \n",
    "- Relevance for features does not have a causal relationship. \n",
    "- Don't be overconfident. \n",
    "    - The methods we have seen probably do not discover the ground truth and how the world really works.\n",
    "    - They simply tell you which features help in predicting $y_i$ for the data you have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (optional) Feature selection and OHE\n",
    "\n",
    "- If you are using OHE with `drop='first'`, and then feature selection, the choice to drop the first is no longer arbitrary.\n",
    "  - Say you have categories A,B,C,D\n",
    "  - you drop='first'\n",
    "  - so then you have B,C,D and 0,0,0 means A.\n",
    "  - Then you feature select out D\n",
    "  - Now you have B,C\n",
    "  - but now 0,0 means A or D.\n",
    "  - So now the feature removed by feature selection becomes mixed up with the \"arbitrary\" feature A."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break (5 min)\n",
    "\n",
    "<br><br><br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Midterm review (30 min)\n",
    "\n",
    "Practice question 1: Describe a scenario where you have an unbalanced training set and **would** want to set `class_weight='balanced'`, and a scenario where you have an unbalanced training set but **wouldn't** want to use it.\n",
    "\n",
    "<br><br><br><br><br><br>\n",
    "\n",
    "Answer (stealing from the chat): if your training data reflects deployment and you are not interested in 1 class over another( and care about accuracy) you wouldn’t want to use `class_weight=“balanced”`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Practice question 2: If we fit a logistic regression on a binary classification problem, how many coefficients will be learned? How can we interpret these coefficients?\n",
    "\n",
    "<br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One per transformed feature. The coefficients tell you how the probability score of the positive class would change when you increased that feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Practice question 3: We tend to split our data into three parts: train, (cross-)validation and test. Name an **advantage** ~~and a **disadvantage**~~ for taking this approach, as opposed to just using two splits (e.g. train and test).\n",
    "\n",
    "<br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Practice question 4: Suppose you train & tune a model and you get a final test score of 95%. Name three reasons why your deployment score might be lower.\n",
    "\n",
    "<br><br><br><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q&A\n",
    "\n",
    "Should we go through last year's midterm?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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