From 13c3621b56ede20034509e7950bde59cfc549cc4 Mon Sep 17 00:00:00 2001 From: samia-boubaya Date: Sun, 1 Mar 2026 16:28:45 +0100 Subject: [PATCH] commit --- X_test.csv | 1323 ++++++++++ X_train.csv | 5285 ++++++++++++++++++++++++++++++++++++++++ lab-hyper-tuning.ipynb | 3229 +++++++++++++++++++++++- y_test.csv | 1323 ++++++++++ y_train.csv | 5285 ++++++++++++++++++++++++++++++++++++++++ 5 files changed, 16436 insertions(+), 9 deletions(-) create mode 100644 X_test.csv create mode 100644 X_train.csv create mode 100644 y_test.csv create mode 100644 y_train.csv diff --git a/X_test.csv b/X_test.csv new file mode 100644 index 0000000..8a59edc --- /dev/null +++ b/X_test.csv @@ -0,0 +1,1323 @@ +Age,RoomService,FoodCourt,ShoppingMall,Spa,VRDeck,CabinNumber,CryoSleep,VIP,HomePlanet_Earth,HomePlanet_Europa,HomePlanet_Mars,Destination_55 Cancri e,Destination_PSO J318.5-22,Destination_TRAPPIST-1e,Deck_A,Deck_B,Deck_C,Deck_D,Deck_E,Deck_F,Deck_G,Deck_T,Side_P,Side_S 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-35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -200,7 +200,7 @@ "4 True " ] }, - "execution_count": 2, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -221,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -317,11 +317,3222 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "# Solved LAB | Hyperparameter Tuning\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Load & View the data**\n", + "\n", + "Finally step in order to maximize the performance on your Spaceship Titanic model.\n", + "\n", + "The data can be found here:\n", + "\n", + "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\n", + "\n", + "Metadata\n", + "\n", + "https://github.com/data-bootcamp-v4/data/blob/main/spaceship_titanic.md" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far we've been training and evaluating models with default values for hyperparameters.\n", + "\n", + "Today we will perform the same feature engineering as before, and then compare the best working models you got so far, but now fine tuning it's hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "#Libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdHomePlanetCryoSleepCabinDestinationAgeVIPRoomServiceFoodCourtShoppingMallSpaVRDeckNameTransported
00001_01EuropaFalseB/0/PTRAPPIST-1e39.0False0.00.00.00.00.0Maham OfracculyFalse
10002_01EarthFalseF/0/STRAPPIST-1e24.0False109.09.025.0549.044.0Juanna VinesTrue
20003_01EuropaFalseA/0/STRAPPIST-1e58.0True43.03576.00.06715.049.0Altark SusentFalse
30003_02EuropaFalseA/0/STRAPPIST-1e33.0False0.01283.0371.03329.0193.0Solam SusentFalse
40004_01EarthFalseF/1/STRAPPIST-1e16.0False303.070.0151.0565.02.0Willy SantantinesTrue
\n", + "
" + ], + "text/plain": [ + " PassengerId HomePlanet CryoSleep Cabin Destination Age VIP \\\n", + "0 0001_01 Europa False B/0/P TRAPPIST-1e 39.0 False \n", + "1 0002_01 Earth False F/0/S TRAPPIST-1e 24.0 False \n", + "2 0003_01 Europa False A/0/S TRAPPIST-1e 58.0 True \n", + "3 0003_02 Europa False A/0/S TRAPPIST-1e 33.0 False \n", + "4 0004_01 Earth False F/1/S TRAPPIST-1e 16.0 False \n", + "\n", + " RoomService FoodCourt ShoppingMall Spa VRDeck Name \\\n", + "0 0.0 0.0 0.0 0.0 0.0 Maham Ofracculy \n", + "1 109.0 9.0 25.0 549.0 44.0 Juanna Vines \n", + "2 43.0 3576.0 0.0 6715.0 49.0 Altark Susent \n", + "3 0.0 1283.0 371.0 3329.0 193.0 Solam Susent \n", + "4 303.0 70.0 151.0 565.0 2.0 Willy Santantines \n", + "\n", + " Transported \n", + "0 False \n", + "1 True \n", + "2 False \n", + "3 False \n", + "4 True " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spaceship = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/spaceship_titanic.csv\")\n", + "spaceship.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 8693 entries, 0 to 8692\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 8693 non-null object \n", + " 1 HomePlanet 8492 non-null object \n", + " 2 CryoSleep 8476 non-null object \n", + " 3 Cabin 8494 non-null object \n", + " 4 Destination 8511 non-null object \n", + " 5 Age 8514 non-null float64\n", + " 6 VIP 8490 non-null object \n", + " 7 RoomService 8512 non-null float64\n", + " 8 FoodCourt 8510 non-null float64\n", + " 9 ShoppingMall 8485 non-null float64\n", + " 10 Spa 8510 non-null float64\n", + " 11 VRDeck 8505 non-null float64\n", + " 12 Name 8493 non-null object \n", + " 13 Transported 8693 non-null bool \n", + "dtypes: bool(1), float64(6), object(7)\n", + "memory usage: 891.5+ KB\n" + ] + } + ], + "source": [ + "spaceship.info()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### **Data Cleaning**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Check the shape of your data**" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset Shape: (8693, 14)\n", + "Number of rows: 8693\n", + "Number of columns: 14\n" + ] + } + ], + "source": [ + "# Check the shape of the dataset*\n", + "print(\"Dataset Shape:\", spaceship.shape)\n", + "# number of rows \n", + "print(\"Number of rows:\", spaceship.shape[0])\n", + "# number of columns\n", + "print(\"Number of columns:\", spaceship.shape[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Check for data types**" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data Types:\n" + ] + }, + { + "data": { + "text/plain": [ + "PassengerId object\n", + "HomePlanet object\n", + "CryoSleep object\n", + "Cabin object\n", + "Destination object\n", + "Age float64\n", + "VIP object\n", + "RoomService float64\n", + "FoodCourt float64\n", + "ShoppingMall float64\n", + "Spa float64\n", + "VRDeck float64\n", + "Name object\n", + "Transported bool\n", + "dtype: object" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Data Types:\")\n", + "display(spaceship.dtypes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Check for missing values**" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing Values:\n" + ] + }, + { + "data": { + "text/plain": [ + "PassengerId 0\n", + "HomePlanet 201\n", + "CryoSleep 217\n", + "Cabin 199\n", + "Destination 182\n", + "Age 179\n", + "VIP 203\n", + "RoomService 181\n", + "FoodCourt 183\n", + "ShoppingMall 208\n", + "Spa 183\n", + "VRDeck 188\n", + "Name 200\n", + "Transported 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Missing Values:\")\n", + "display(spaceship.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are multiple strategies to handle missing data\n", + "\n", + "- Removing all rows or all columns containing missing data.\n", + "- Filling all missing values with a value (mean in continouos or mode in categorical for example).\n", + "- Filling all missing values with an algorithm.\n", + "\n", + "For this exercise, because we have such low amount of null values, we will drop rows containing any missing value. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Deal with Nulls**" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing values in 'PassengerId': 0\n", + "Missing values in 'PassengerId' after dropping nulls: 0\n", + "Missing values in 'HomePlanet': 201\n", + "Missing values in 'HomePlanet' after dropping nulls: 0\n", + "Missing values in 'CryoSleep': 215\n", + "Missing values in 'CryoSleep' after dropping nulls: 0\n", + "Missing values in 'Cabin': 186\n", + "Missing values in 'Cabin' after dropping nulls: 0\n", + "Missing values in 'Destination': 171\n", + "Missing values in 'Destination' after dropping nulls: 0\n", + "Missing values in 'Age': 169\n", + "Missing values in 'Age' after dropping nulls: 0\n", + "Missing values in 'VIP': 179\n", + "Missing values in 'VIP' after dropping nulls: 0\n", + "Missing values in 'RoomService': 161\n", + "Missing values in 'RoomService' after dropping nulls: 0\n", + "Missing values in 'FoodCourt': 159\n", + "Missing values in 'FoodCourt' after dropping nulls: 0\n", + "Missing values in 'ShoppingMall': 173\n", + "Missing values in 'ShoppingMall' after dropping nulls: 0\n", + "Missing values in 'Spa': 161\n", + "Missing values in 'Spa' after dropping nulls: 0\n", + "Missing values in 'VRDeck': 154\n", + "Missing values in 'VRDeck' after dropping nulls: 0\n", + "Missing values in 'Name': 158\n", + "Missing values in 'Name' after dropping nulls: 0\n", + "Missing values in 'Transported': 0\n", + "Missing values in 'Transported' after dropping nulls: 0\n" + ] + } + ], + "source": [ + "# handle missing values\n", + "'''\n", + "spaceship[\"HomePlanet\"] = spaceship[\"HomePlanet\"].fillna(\"Unknown\") # fill missing values with \"Unknown\"\n", + "spaceship[\"CryoSleep\"] = spaceship[\"CryoSleep\"].fillna(False)\n", + "spaceship[\"Destination\"] = spaceship[\"Destination\"].fillna(\"Unknown\") # fill missing values with \"Unknown\"\n", + "spaceship[\"Age\"] = spaceship[\"Age\"].fillna(spaceship[\"Age\"].median()) # fill missing values with median before converting to int\n", + "spaceship[\"RoomService\"] = spaceship[\"RoomService\"].fillna(0) # fill missing values with 0\n", + "spaceship[\"FoodCourt\"] = spaceship[\"FoodCourt\"].fillna(0) # fill missing values with 0\n", + "spaceship[\"ShoppingMall\"] = spaceship[\"ShoppingMall\"].fillna(0) # fill missing values with 0\n", + "spaceship[\"Spa\"] = spaceship[\"Spa\"].fillna(0) # fill missing values with 0\n", + "spaceship[\"VRDeck\"] = spaceship[\"VRDeck\"].fillna(0) # fill missing values with 0\n", + "spaceship[\"Cabin\"] = spaceship[\"Cabin\"].fillna(\"Unknown\") # fill missing values with \"Unknown\"\n", + "'''\n", + "\n", + "# drop nulls in spaceship dataset\n", + "for col in spaceship.columns:\n", + " print(f\"Missing values in '{col}': {spaceship[col].isnull().sum()}\")\n", + " # if there are missing values, drop the rows with missing values in that column\n", + " if spaceship[col].isnull().sum() > 0:\n", + " spaceship = spaceship.dropna(subset=[col])\n", + " print(f\"Missing values in '{col}' after dropping nulls: {spaceship[col].isnull().sum()}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Deal with Duplicates**" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# remove duplicates\n", + "spaceship = spaceship.drop_duplicates()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### **Data Preparation**\n", + "Now perform the same as before:\n", + "- Feature Scaling\n", + "- Feature Selection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Split **Cabin** to \"Deck\", \"CabinNumber\", and \"Side\"" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "unique values in 'Cabin' column before splitting: ['B/0/P' 'F/0/S' 'A/0/S' ... 'G/1499/S' 'G/1500/S' 'E/608/S']\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------------------------------------------------------------------\n", + "unique values in 'Deck' column after splitting: ['B' 'F' 'A' 'G' 'E' 'C' 'D' 'T']\n", + "unique values in 'CabinNumber' column after splitting: ['0' '1' '2' ... '1894' '1499' '608']\n", + "unique values in 'Side' column after splitting: ['P' 'S']\n" + ] + } + ], + "source": [ + "# check the unique values in the 'Cabin' column\n", + "print(\"unique values in 'Cabin' column before splitting:\", spaceship['Cabin'].unique())\n", + "\n", + "# split the 'Cabin' column into 'Deck', 'CabinNumber', and 'Side'\n", + "spaceship[['Deck', 'CabinNumber', 'Side']] = spaceship['Cabin'].str.split('/', expand=True)\n", + "\n", + "# drop the original 'Cabin' column\n", + "spaceship = spaceship.drop('Cabin', axis=1)\n", + "print(100*'-')\n", + "# check the unique values in the new columns\n", + "print(\"unique values in 'Deck' column after splitting:\", spaceship['Deck'].unique())\n", + "print(\"unique values in 'CabinNumber' column after splitting:\", spaceship['CabinNumber'].unique())\n", + "print(\"unique values in 'Side' column after splitting:\", spaceship['Side'].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Drop PassengerId and Name**" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "# drop PassengerId and Name columns since they are not useful for modeling\n", + "spaceship.drop([\"PassengerId\", \"Name\"], axis=1, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Convert Data Types**" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 6606 entries, 0 to 8692\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 HomePlanet 6606 non-null object \n", + " 1 CryoSleep 6606 non-null object \n", + " 2 Destination 6606 non-null object \n", + " 3 Age 6606 non-null float64\n", + " 4 VIP 6606 non-null object \n", + " 5 RoomService 6606 non-null float64\n", + " 6 FoodCourt 6606 non-null float64\n", + " 7 ShoppingMall 6606 non-null float64\n", + " 8 Spa 6606 non-null float64\n", + " 9 VRDeck 6606 non-null float64\n", + " 10 Transported 6606 non-null bool \n", + " 11 Deck 6606 non-null object \n", + " 12 CabinNumber 6606 non-null object \n", + " 13 Side 6606 non-null object \n", + "dtypes: bool(1), float64(6), object(7)\n", + "memory usage: 729.0+ KB\n" + ] + } + ], + "source": [ + "spaceship.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# convert Age to integer type\n", + "spaceship[\"Age\"] = spaceship[\"Age\"].astype(int)\n", + "\n", + "# convert VIP to boolean type\n", + "spaceship[\"VIP\"] = spaceship[\"VIP\"].astype(bool)\n", + "\n", + "# convert booleans to integers (True=1, False=0) for modeling\n", + "for col in spaceship.columns:\n", + " if spaceship[col].dtype == 'bool':\n", + " spaceship[col] = spaceship[col].astype(int)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### **Perform Train Test Split**\n", + "- split spaceship dataset into train and test sets (stratify by target variable to maintain class distribution)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Define target (y)**" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "# define target variable\n", + "y = spaceship[\"Transported\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Define features (X)**" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of data after dropping 'Transported', 'PassengerId', 'Name': (6606, 13)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " HomePlanet CryoSleep Destination Age VIP RoomService FoodCourt \\\n", + "0 Europa False TRAPPIST-1e 39 0 0.0 0.0 \n", + "1 Earth False TRAPPIST-1e 24 0 109.0 9.0 \n", + "2 Europa False TRAPPIST-1e 58 1 43.0 3576.0 \n", + "3 Europa False TRAPPIST-1e 33 0 0.0 1283.0 \n", + "4 Earth False TRAPPIST-1e 16 0 303.0 70.0 \n", + "\n", + " ShoppingMall Spa VRDeck Deck CabinNumber Side \n", + "0 0.0 0.0 0.0 B 0 P \n", + "1 25.0 549.0 44.0 F 0 S \n", + "2 0.0 6715.0 49.0 A 0 S \n", + "3 371.0 3329.0 193.0 A 0 S \n", + "4 151.0 565.0 2.0 F 1 S " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# define features: \n", + "X = spaceship.drop([\"Transported\"], axis=1)\n", + "print(f\"Shape of data after dropping 'Transported', 'PassengerId', 'Name': {X.shape}\")\n", + "display(X.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**SPLIT**" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape: (5284, 13)\n", + "X_test shape: (1322, 13)\n", + "y_train shape: (5284,)\n", + "y_test shape: (1322,)\n" + ] + } + ], + "source": [ + "# define train-test split sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n", + "\n", + "#display the shapes of the resulting sets\n", + "print(\"X_train shape:\", X_train.shape)\n", + "print(\"X_test shape:\", X_test.shape)\n", + "print(\"y_train shape:\", y_train.shape)\n", + "print(\"y_test shape:\", y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### **Feature Engineering**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Identity Categorical vs Numerical Features" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", + "\n", + "# Boolean columns (already 0/1)\n", + "bool_cols = [\"CryoSleep\", \"VIP\"]\n", + "\n", + "# Categorical columns (more than 2 categories)\n", + "cat_cols = [\"HomePlanet\", \"Destination\", \"Deck\", \"Side\"]\n", + "\n", + "# Numerical columns\n", + "num_cols = [\"Age\", \"RoomService\", \"FoodCourt\", \"ShoppingMall\", \"Spa\", \"VRDeck\", \"CabinNumber\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. one hot encode categorical features (not boolean)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "ohe = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n", + "\n", + "X_train_cat = pd.DataFrame(\n", + " ohe.fit_transform(X_train[cat_cols]),\n", + " columns=ohe.get_feature_names_out(cat_cols),\n", + " index=X_train.index\n", + ")\n", + "\n", + "X_test_cat = pd.DataFrame(\n", + " ohe.transform(X_test[cat_cols]),\n", + " columns=ohe.get_feature_names_out(cat_cols),\n", + " index=X_test.index\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Scale Numerical Columns" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "scaler = StandardScaler()\n", + "\n", + "X_train_num = pd.DataFrame(\n", + " scaler.fit_transform(X_train[num_cols]),\n", + " columns=num_cols,\n", + " index=X_train.index\n", + ")\n", + "\n", + "X_test_num = pd.DataFrame(\n", + " scaler.transform(X_test[num_cols]),\n", + " columns=num_cols,\n", + " index=X_test.index\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Convert Booleans to int (True=1 , False=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# Boolean columns (already 0/1)\n", + "X_train_bool = X_train[bool_cols]\n", + "X_test_bool = X_test[bool_cols]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "5. Combine & Save Train Test sets CSVs" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X_train shape after encoding and scaling: (5284, 25)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Age RoomService FoodCourt ShoppingMall Spa VRDeck \\\n", + "8153 0.280747 -0.342297 1.578894 -0.299472 -0.272763 0.603416 \n", + "7374 -0.201234 -0.342297 -0.286602 -0.299472 -0.275405 -0.272281 \n", + "8231 -1.716031 -0.342297 -0.286602 -0.299472 -0.275405 -0.272281 \n", + "5795 -1.784885 -0.342297 -0.286602 -0.299472 -0.275405 -0.272281 \n", + "2536 -0.338942 -0.342297 -0.286602 -0.299472 -0.275405 -0.272281 \n", + "\n", + " CabinNumber CryoSleep VIP HomePlanet_Earth ... Deck_A Deck_B \\\n", + "8153 -0.956374 False 1 0.0 ... 1.0 0.0 \n", + "7374 1.317833 True 0 1.0 ... 0.0 0.0 \n", + "8231 1.599921 True 0 1.0 ... 0.0 0.0 \n", + "5795 0.775059 True 0 1.0 ... 0.0 0.0 \n", + "2536 -0.966101 True 0 0.0 ... 0.0 0.0 \n", + "\n", + " Deck_C Deck_D Deck_E Deck_F Deck_G Deck_T Side_P Side_S \n", + "8153 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "7374 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n", + "8231 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "5795 0.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 \n", + "2536 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_train shape: (5284,)\n" + ] + }, + { + "data": { + "text/plain": [ + "6017 0\n", + "8533 0\n", + "6961 1\n", + "2248 0\n", + "2422 0\n", + "Name: Transported, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y_test shape: (1322,)\n" + ] + }, + { + "data": { + "text/plain": [ + "8153 0\n", + "7374 1\n", + "8231 0\n", + "5795 1\n", + "2536 1\n", + "Name: Transported, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Combine all features\n", + "X_train = pd.concat([X_train_num, X_train_bool, X_train_cat], axis=1)\n", + "X_test = pd.concat([X_test_num, X_test_bool, X_test_cat], axis=1)\n", + "\n", + "# display the shapes of the resulting sets\n", + "print(\"X_train shape after encoding and scaling:\", X_train.shape)\n", + "display(X_train.head())\n", + "print(\"X_test shape after encoding and scaling:\", X_test.shape)\n", + "display(X_test.head())\n", + "\n", + "print(\"y_train shape:\", y_train.shape)\n", + "display(y_train.head())\n", + "print(\"y_test shape:\", y_test.shape)\n", + "display(y_test.head())\n", + "\n", + "# ---- Save preprocessed data to CSV files ----\n", + "X_train.to_csv(\"X_train.csv\", index=False)\n", + "X_test.to_csv(\"X_test.csv\", index=False)\n", + "y_train.to_csv(\"y_train.csv\", index=False)\n", + "y_test.to_csv(\"y_test.csv\", index=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### **Model Selection** \n", + "now you will try to apply different ensemble methods in order to get a better model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "**To improve model performance, several ensemble methods were applied and compared.**\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1️⃣ **Bagging and Pasting**\n", + "\n", + "- Bagging (Bootstrap Aggregating) trains multiple base models on random subsets of the data.\n", + "- Bagging: Sampling with replacement\n", + "- Pasting: Sampling without replacement\n", + "- Base learner: Decision Tree\n", + "\n", + "These methods reduce variance and help prevent overfitting.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### **Bagging (with replacement)**" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== BAGGING RESULTS ===\n", + "Train Accuracy: 0.9998107494322483\n", + "Test Accuracy : 0.8018154311649016\n", + "\n", + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.78 0.83 0.81 656\n", + " 1 0.82 0.77 0.80 666\n", + "\n", + " accuracy 0.80 1322\n", + " macro avg 0.80 0.80 0.80 1322\n", + "weighted avg 0.80 0.80 0.80 1322\n", + "\n", + "PR AUC: 0.9010410076421606\n", + "Train ROC AUC: 0.9999987822005865\n", + "Test ROC AUC : 0.8853628781220246\n", + "\n", + "Confusion Matrix:\n", + "\n", + "[[546 110]\n", + " [152 514]]\n", + "\n", + "Accuracy Gap: 0.19799531826734673\n", + "ROC AUC Gap: 0.11463590407856183\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import BaggingClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.metrics import (\n", + " accuracy_score,\n", + " classification_report,\n", + " average_precision_score,\n", + " roc_auc_score,\n", + " confusion_matrix\n", + ")\n", + "\n", + "# Base model\n", + "dt = DecisionTreeClassifier(random_state=42)\n", + "\n", + "# Bagging\n", + "bagging = BaggingClassifier(\n", + " estimator=dt,\n", + " n_estimators=100,\n", + " bootstrap=True,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "bagging.fit(X_train, y_train)\n", + "\n", + "# Predictions\n", + "y_train_pred = bagging.predict(X_train)\n", + "y_test_pred = bagging.predict(X_test)\n", + "\n", + "y_train_proba = bagging.predict_proba(X_train)[:, 1]\n", + "y_test_proba = bagging.predict_proba(X_test)[:, 1]\n", + "\n", + "# -----------------------\n", + "# Important Metrics\n", + "# -----------------------\n", + "\n", + "print(\"=== BAGGING RESULTS ===\")\n", + "\n", + "# Accuracy\n", + "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n", + "print(\"Test Accuracy :\", accuracy_score(y_test, y_test_pred))\n", + "\n", + "# Classification Report\n", + "print(\"\\nClassification Report:\\n\")\n", + "print(classification_report(y_test, y_test_pred))\n", + "\n", + "# PR AUC\n", + "print(\"PR AUC:\", average_precision_score(y_test, y_test_proba))\n", + "\n", + "# ROC AUC\n", + "print(\"Train ROC AUC:\", roc_auc_score(y_train, y_train_proba))\n", + "print(\"Test ROC AUC :\", roc_auc_score(y_test, y_test_proba))\n", + "\n", + "# Confusion Matrix\n", + "print(\"\\nConfusion Matrix:\\n\")\n", + "print(confusion_matrix(y_test, y_test_pred))\n", + "\n", + "# Overfitting Check\n", + "print(\"\\nAccuracy Gap:\", \n", + " accuracy_score(y_train, y_train_pred) - accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"ROC AUC Gap:\", \n", + " roc_auc_score(y_train, y_train_proba) - roc_auc_score(y_test, y_test_proba))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### **Pasting (without replacement)**" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== PASTING RESULTS ===\n", + "Train Accuracy: 0.9998107494322483\n", + "Test Accuracy : 0.7549167927382754\n", + "\n", + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.76 0.73 0.75 656\n", + " 1 0.75 0.78 0.76 666\n", + "\n", + " accuracy 0.75 1322\n", + " macro avg 0.76 0.75 0.75 1322\n", + "weighted avg 0.76 0.75 0.75 1322\n", + "\n", + "PR AUC: 0.738637372148855\n", + "Train ROC AUC: 0.9999999283647404\n", + "Test ROC AUC : 0.789088249835201\n", + "\n", + "Confusion Matrix:\n", + "\n", + "[[481 175]\n", + " [149 517]]\n", + "\n", + "Accuracy Gap: 0.24489395669397296\n", + "ROC AUC Gap: 0.2109116785295394\n" + ] + } + ], + "source": [ + "pasting = BaggingClassifier(\n", + " estimator=dt,\n", + " n_estimators=100,\n", + " bootstrap=False,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "pasting.fit(X_train, y_train)\n", + "\n", + "y_train_pred = pasting.predict(X_train)\n", + "y_test_pred = pasting.predict(X_test)\n", + "\n", + "y_train_proba = pasting.predict_proba(X_train)[:, 1]\n", + "y_test_proba = pasting.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"=== PASTING RESULTS ===\")\n", + "\n", + "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n", + "print(\"Test Accuracy :\", accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"\\nClassification Report:\\n\")\n", + "print(classification_report(y_test, y_test_pred))\n", + "\n", + "print(\"PR AUC:\", average_precision_score(y_test, y_test_proba))\n", + "\n", + "print(\"Train ROC AUC:\", roc_auc_score(y_train, y_train_proba))\n", + "print(\"Test ROC AUC :\", roc_auc_score(y_test, y_test_proba))\n", + "\n", + "print(\"\\nConfusion Matrix:\\n\")\n", + "print(confusion_matrix(y_test, y_test_pred))\n", + "\n", + "print(\"\\nAccuracy Gap:\", \n", + " accuracy_score(y_train, y_train_pred) - accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"ROC AUC Gap:\", \n", + " roc_auc_score(y_train, y_train_proba) - roc_auc_score(y_test, y_test_proba))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **2️⃣ Random Forests**\n", + "\n", + "Random Forest is an extension of Bagging that:\n", + "- Uses Decision Trees\n", + "- Applies bootstrap sampling\n", + "- Randomly selects features at each split\n", + "\n", + "This improves generalization and reduces correlation between trees." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== RANDOM FOREST RESULTS ===\n", + "Train Accuracy: 0.9998107494322483\n", + "Test Accuracy : 0.7987897125567323\n", + "\n", + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.78 0.83 0.80 656\n", + " 1 0.82 0.76 0.79 666\n", + "\n", + " accuracy 0.80 1322\n", + " macro avg 0.80 0.80 0.80 1322\n", + "weighted avg 0.80 0.80 0.80 1322\n", + "\n", + "PR AUC: 0.9003160032373015\n", + "Train ROC AUC: 0.999999641823702\n", + "Test ROC AUC : 0.8868449241924852\n", + "\n", + "Confusion Matrix:\n", + "\n", + "[[547 109]\n", + " [157 509]]\n", + "\n", + "Accuracy Gap: 0.2010210368755161\n", + "ROC AUC Gap: 0.11315471763121687\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "rf = RandomForestClassifier(\n", + " n_estimators=100,\n", + " random_state=42,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "rf.fit(X_train, y_train)\n", + "\n", + "y_train_pred = rf.predict(X_train)\n", + "y_test_pred = rf.predict(X_test)\n", + "\n", + "y_train_proba = rf.predict_proba(X_train)[:, 1]\n", + "y_test_proba = rf.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"=== RANDOM FOREST RESULTS ===\")\n", + "\n", + "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n", + "print(\"Test Accuracy :\", accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"\\nClassification Report:\\n\")\n", + "print(classification_report(y_test, y_test_pred))\n", + "\n", + "print(\"PR AUC:\", average_precision_score(y_test, y_test_proba))\n", + "\n", + "print(\"Train ROC AUC:\", roc_auc_score(y_train, y_train_proba))\n", + "print(\"Test ROC AUC :\", roc_auc_score(y_test, y_test_proba))\n", + "\n", + "print(\"\\nConfusion Matrix:\\n\")\n", + "print(confusion_matrix(y_test, y_test_pred))\n", + "\n", + "print(\"\\nAccuracy Gap:\",\n", + " accuracy_score(y_train, y_train_pred) - accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"ROC AUC Gap:\",\n", + " roc_auc_score(y_train, y_train_proba) - roc_auc_score(y_test, y_test_proba))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **3️⃣ Gradient Boosting**\n", + "\n", + "Gradient Boosting builds trees sequentially:\n", + "- Each new tree corrects the errors of the previous one\n", + "- Focuses on minimizing a loss function\n", + "- Reduces bias\n", + "\n", + "It typically achieves higher predictive accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== GRADIENT BOOSTING RESULTS ===\n", + "Train Accuracy: 0.8296744890234671\n", + "Test Accuracy : 0.8040847201210287\n", + "\n", + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.83 0.76 0.79 656\n", + " 1 0.78 0.85 0.81 666\n", + "\n", + " accuracy 0.80 1322\n", + " macro avg 0.81 0.80 0.80 1322\n", + "weighted avg 0.81 0.80 0.80 1322\n", + "\n", + "PR AUC: 0.9120267229141965\n", + "Train ROC AUC: 0.9256837907889377\n", + "Test ROC AUC : 0.9016413517175712\n", + "\n", + "Confusion Matrix:\n", + "\n", + "[[498 158]\n", + " [101 565]]\n", + "\n", + "Accuracy Gap: 0.02558976890243836\n", + "ROC AUC Gap: 0.024042439071366495\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "\n", + "gb = GradientBoostingClassifier(\n", + " n_estimators=100,\n", + " learning_rate=0.1,\n", + " random_state=42\n", + ")\n", + "\n", + "gb.fit(X_train, y_train)\n", + "\n", + "y_train_pred = gb.predict(X_train)\n", + "y_test_pred = gb.predict(X_test)\n", + "\n", + "y_train_proba = gb.predict_proba(X_train)[:, 1]\n", + "y_test_proba = gb.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"=== GRADIENT BOOSTING RESULTS ===\")\n", + "\n", + "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n", + "print(\"Test Accuracy :\", accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"\\nClassification Report:\\n\")\n", + "print(classification_report(y_test, y_test_pred))\n", + "\n", + "print(\"PR AUC:\", average_precision_score(y_test, y_test_proba))\n", + "\n", + "print(\"Train ROC AUC:\", roc_auc_score(y_train, y_train_proba))\n", + "print(\"Test ROC AUC :\", roc_auc_score(y_test, y_test_proba))\n", + "\n", + "print(\"\\nConfusion Matrix:\\n\")\n", + "print(confusion_matrix(y_test, y_test_pred))\n", + "\n", + "print(\"\\nAccuracy Gap:\",\n", + " accuracy_score(y_train, y_train_pred) - accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"ROC AUC Gap:\",\n", + " roc_auc_score(y_train, y_train_proba) - roc_auc_score(y_test, y_test_proba))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **4️⃣ Adaptive Boosting (AdaBoost)**\n", + "\n", + "AdaBoost:\n", + "- Trains weak learners sequentially\n", + "- Increases weight on misclassified samples\n", + "- Combines models using weighted voting\n", + "\n", + "It improves performance by focusing on difficult observations." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== ADABOOST RESULTS ===\n", + "Train Accuracy: 0.7469719909159728\n", + "Test Accuracy : 0.7299546142208775\n", + "\n", + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.68 0.86 0.76 656\n", + " 1 0.81 0.60 0.69 666\n", + "\n", + " accuracy 0.73 1322\n", + " macro avg 0.75 0.73 0.73 1322\n", + "weighted avg 0.75 0.73 0.73 1322\n", + "\n", + "PR AUC: 0.8249109623222503\n", + "Train ROC AUC: 0.8513073363245353\n", + "Test ROC AUC : 0.834081795209844\n", + "\n", + "Confusion Matrix:\n", + "\n", + "[[563 93]\n", + " [264 402]]\n", + "\n", + "Accuracy Gap: 0.017017376695095288\n", + "ROC AUC Gap: 0.017225541114691212\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import AdaBoostClassifier\n", + "\n", + "ada = AdaBoostClassifier(\n", + " n_estimators=100,\n", + " learning_rate=0.1,\n", + " random_state=42\n", + ")\n", + "\n", + "ada.fit(X_train, y_train)\n", + "\n", + "y_train_pred = ada.predict(X_train)\n", + "y_test_pred = ada.predict(X_test)\n", + "\n", + "y_train_proba = ada.predict_proba(X_train)[:, 1]\n", + "y_test_proba = ada.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"=== ADABOOST RESULTS ===\")\n", + "\n", + "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n", + "print(\"Test Accuracy :\", accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"\\nClassification Report:\\n\")\n", + "print(classification_report(y_test, y_test_pred))\n", + "\n", + "print(\"PR AUC:\", average_precision_score(y_test, y_test_proba))\n", + "\n", + "print(\"Train ROC AUC:\", roc_auc_score(y_train, y_train_proba))\n", + "print(\"Test ROC AUC :\", roc_auc_score(y_test, y_test_proba))\n", + "\n", + "print(\"\\nConfusion Matrix:\\n\")\n", + "print(confusion_matrix(y_test, y_test_pred))\n", + "\n", + "print(\"\\nAccuracy Gap:\",\n", + " accuracy_score(y_train, y_train_pred) - accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"ROC AUC Gap:\",\n", + " roc_auc_score(y_train, y_train_proba) - roc_auc_score(y_test, y_test_proba))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "### **Compare Results**\n", + "Which model is the best and why?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**🎯 Metrics I Prioritized for Classification, I prioritize:**\n", + "\n", + "1️⃣ Test ROC AUC (Most Important)\n", + "- Measures ranking quality\n", + "- Threshold independent\n", + "- Best overall indicator of model quality\n", + "\n", + "2️⃣ Test Accuracy\n", + "- Easy interpretability\n", + "- Good when classes are balanced (yours are balanced)\n", + "\n", + "3️⃣ Overfitting Gap (Train vs Test)\n", + "- Small gap = better generalization\n", + "- Large gap = overfitting\n", + "- PR AUC and F1 were supportive but secondary.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "- Bagging & Random Forest almost perfectly fit training data (Train ROC ≈ 1.0)\n", + "- That’s a classic sign of deep trees overfitting\n", + "- Gradient Boosting avoided that and generalized bette\n", + "\n", + "- Gradient Boosting performed best based on highest Test ROC AUC, PR AUC, and smallest overfitting gap.\n", + "- Random Forest and Bagging performed similarly but showed strong overfitting.\n", + "- Pasting performed worst across all evaluation metrics.\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "| Model | Test Acc | Test ROC | PR AUC | Overfitting | Rank |\n", + "|---------------------|----------|----------|--------|-------------|------|\n", + "| Gradient Boosting | *0.804* | *0.902* | *0.912* | Very Low | 🥇 |\n", + "| Random Forest | 0.799 | 0.887 | 0.900 | High | 🥈 |\n", + "| Bagging | 0.802 | 0.885 | 0.901 | High | 🥉 |\n", + "| Pasting | 0.755 | 0.789 | 0.739 | Very High | 4️⃣ |" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "#### **🏆 Final Ranking (Best → Worst)**\n", + "---\n", + "**🥇 Gradient Boosting → BEST**\n", + "- Highest Test ROC AUC: 0.902\n", + "- Highest PR AUC: 0.912\n", + "- Highest Test Accuracy: 0.804\n", + "- Very small overfitting gap (~0.026)\n", + "- Balanced precision/recall\n", + "- This model generalizes extremely well.\n", + "\n", + "Conclusion: Most stable + best predictive power.\n", + "\n", + "---\n", + "**🥈 Random Forest**\n", + "- Test ROC AUC: 0.887\n", + "- Test Accuracy: 0.799\n", + "- PR AUC: 0.900\n", + "- Large overfitting (ROC gap ~0.113)\n", + "- Very strong performance but clearly overfitting.\n", + "---\n", + "\n", + "**🥉 Bagging**\n", + "- Test ROC AUC: 0.885\n", + "- Test Accuracy: 0.802\n", + "- PR AUC: 0.901\n", + "- Large overfitting (ROC gap ~0.115)\n", + "- Very similar to Random Forest but slightly worse ROC.\n", + "---\n", + "**4️⃣ Pasting → WORST**\n", + "- Test ROC AUC: 0.789\n", + "- Test Accuracy: 0.755\n", + "- PR AUC: 0.739\n", + "- Massive overfitting (ROC gap ~0.211)\n", + "- Weakest model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## **Hyper Tuning the BEST MODEL**\n", + "\n", + "Now let's use the best model we got so far in order to see how it can improve when we fine tune it's hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tune the most important hyperparameters:\n", + "- `n_estimators`\n", + "- `learning_rate`\n", + "- `max_depth`\n", + "- `min_samples_split`\n", + "- `min_samples_leaf`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Define Hyperparameter Grid**" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "# Define base model\n", + "gb = GradientBoostingClassifier(\n", + " n_estimators=100,\n", + " learning_rate=0.1,\n", + " random_state=42\n", + ")\n", + "\n", + "# Define parameter grid\n", + "param_grid = {\n", + " \"n_estimators\": [100, 200, 300],\n", + " \"learning_rate\": [0.01, 0.05, 0.1],\n", + " \"max_depth\": [3, 4, 5],\n", + " \"min_samples_split\": [2, 5, 10],\n", + " \"min_samples_leaf\": [1, 3, 5]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Grid/Random Search**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this lab we will use Grid Search." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Define hyperparameters to fine tune." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Subsample: use GridSearchCV and optimize for ROC AUC." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Create GridSearch (Setup GridSearchCV)**" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Create GridSearch\n", + "grid_search = GridSearchCV(\n", + " estimator=gb,\n", + " param_grid=param_grid,\n", + " cv=5,\n", + " scoring=\"roc_auc\", # we optimize ROC AUC\n", + " n_jobs=-1,\n", + " verbose=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Run Grid Search (Train set)**" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 243 candidates, totalling 1215 fits\n" + ] + }, + { + "data": { + "text/html": [ + "
GridSearchCV(cv=5, estimator=GradientBoostingClassifier(random_state=42),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'learning_rate': [0.01, 0.05, 0.1],\n",
+       "                         'max_depth': [3, 4, 5], 'min_samples_leaf': [1, 3, 5],\n",
+       "                         'min_samples_split': [2, 5, 10],\n",
+       "                         'n_estimators': [100, 200, 300]},\n",
+       "             scoring='roc_auc', verbose=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "GridSearchCV(cv=5, estimator=GradientBoostingClassifier(random_state=42),\n", + " n_jobs=-1,\n", + " param_grid={'learning_rate': [0.01, 0.05, 0.1],\n", + " 'max_depth': [3, 4, 5], 'min_samples_leaf': [1, 3, 5],\n", + " 'min_samples_split': [2, 5, 10],\n", + " 'n_estimators': [100, 200, 300]},\n", + " scoring='roc_auc', verbose=1)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid_search.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Get Best Results (Evaluate the model)**" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Parameters:\n", + "{'learning_rate': 0.05, 'max_depth': 4, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 300}\n", + "\n", + "Best CV ROC AUC:\n", + "0.9010975260296945\n" + ] + } + ], + "source": [ + "print(\"Best Parameters:\")\n", + "print(grid_search.best_params_)\n", + "\n", + "print(\"\\nBest CV ROC AUC:\")\n", + "print(grid_search.best_score_)\n", + "\n", + "best_gb = grid_search.best_estimator_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Evaluate the model AFTER TUNING**" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=== TUNED GRADIENT BOOSTING RESULTS ===\n", + "Train Accuracy: 0.8732021196063589\n", + "Test Accuracy : 0.8071104387291982\n", + "\n", + "Classification Report:\n", + "\n", + " precision recall f1-score support\n", + "\n", + " 0 0.82 0.79 0.80 656\n", + " 1 0.80 0.83 0.81 666\n", + "\n", + " accuracy 0.81 1322\n", + " macro avg 0.81 0.81 0.81 1322\n", + "weighted avg 0.81 0.81 0.81 1322\n", + "\n", + "PR AUC: 0.9138138142769345\n", + "Train ROC AUC: 0.9530803376542288\n", + "Test ROC AUC : 0.9026587563172929\n", + "\n", + "Confusion Matrix:\n", + "\n", + "[[517 139]\n", + " [116 550]]\n", + "\n", + "Accuracy Gap: 0.06609168087716066\n", + "ROC AUC Gap: 0.05042158133693597\n" + ] + } + ], + "source": [ + "from sklearn.metrics import (\n", + " accuracy_score,\n", + " classification_report,\n", + " average_precision_score,\n", + " roc_auc_score,\n", + " confusion_matrix\n", + ")\n", + "\n", + "# Predictions\n", + "y_train_pred = best_gb.predict(X_train)\n", + "y_test_pred = best_gb.predict(X_test)\n", + "\n", + "y_train_proba = best_gb.predict_proba(X_train)[:, 1]\n", + "y_test_proba = best_gb.predict_proba(X_test)[:, 1]\n", + "\n", + "print(\"=== TUNED GRADIENT BOOSTING RESULTS ===\")\n", + "\n", + "# Accuracy\n", + "print(\"Train Accuracy:\", accuracy_score(y_train, y_train_pred))\n", + "print(\"Test Accuracy :\", accuracy_score(y_test, y_test_pred))\n", + "\n", + "# Classification Report\n", + "print(\"\\nClassification Report:\\n\")\n", + "print(classification_report(y_test, y_test_pred))\n", + "\n", + "# PR AUC\n", + "print(\"PR AUC:\", average_precision_score(y_test, y_test_proba))\n", + "\n", + "# ROC AUC\n", + "print(\"Train ROC AUC:\", roc_auc_score(y_train, y_train_proba))\n", + "print(\"Test ROC AUC :\", roc_auc_score(y_test, y_test_proba))\n", + "\n", + "# Confusion Matrix\n", + "print(\"\\nConfusion Matrix:\\n\")\n", + "print(confusion_matrix(y_test, y_test_pred))\n", + "\n", + "# Overfitting Check\n", + "print(\"\\nAccuracy Gap:\",\n", + " accuracy_score(y_train, y_train_pred) - accuracy_score(y_test, y_test_pred))\n", + "\n", + "print(\"ROC AUC Gap:\",\n", + " roc_auc_score(y_train, y_train_proba) - roc_auc_score(y_test, y_test_proba))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 📊 Gradient Boosting: Before vs After Hyperparameter Tuning\n", + "\n", + "#### Performance Comparison\n", + "\n", + "| Metric | Before Tuning | After Tuning | Change |\n", + "|--------|--------------|--------------|--------|\n", + "| **Test Accuracy** | 0.8041 | **0.8071** | ⬆ +0.003 |\n", + "| **PR AUC** | 0.9120 | **0.9138** | ⬆ +0.0018 |\n", + "| **Test ROC AUC** | 0.9016 | **0.9027** | ⬆ +0.0010 |\n", + "| **Train Accuracy** | 0.8297 | 0.8732 | ⬆ Increased |\n", + "| **Train ROC AUC** | 0.9257 | 0.9531 | ⬆ Increased |\n", + "| **Accuracy Gap** | 0.0256 | 0.0661 | ⬆ Worse |\n", + "| **ROC AUC Gap** | 0.0240 | 0.0504 | ⬆ Worse |\n", + "\n", + "#### 🔎 Interpretation\n", + "✅ Improvements After Tuning\n", + "- Slight increase in **Test Accuracy**\n", + "- Slight increase in **PR AUC**\n", + "- Slight increase in **Test ROC AUC**\n", + "- Better balance between precision and recall\n", + "- Small improvement in confusion matrix distribution\n", + "\n", + "⚠️ Downsides After Tuning\n", + "- Overfitting increased\n", + "- Accuracy gap more than doubled\n", + "- ROC AUC gap doubled\n", + "- Train performance increased significantly while test improvement was minimal\n", + "\n", + "#### 🎯 Conclusion\n", + "\n", + "Hyperparameter tuning resulted in **minor performance improvements** on the test set. \n", + "However, it also increased the gap between training and testing metrics, indicating **higher overfitting**.\n", + "\n", + "- If prioritizing **maximum predictive performance → Tuned model**\n", + "- If prioritizing **generalization and stability → Original model**\n", + "\n", + "Overall, tuning provided marginal gains at the cost of increased model complexity." + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -335,7 +3546,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.13.9" } }, "nbformat": 4, diff --git a/y_test.csv b/y_test.csv new file mode 100644 index 0000000..856f0b5 --- /dev/null +++ b/y_test.csv @@ -0,0 +1,1323 @@ +Transported +0 +1 +0 +1 +1 +1 +0 +0 +1 +0 +0 +0 +0 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