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Solutions | Lab Ensemble Methods #140

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190 changes: 171 additions & 19 deletions lab-ensemble.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -35,10 +35,14 @@
"metadata": {},
"outputs": [],
"source": [
"#Libraries\n",
"# Importar bibliotecas\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split"
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n"
]
},
{
Expand Down Expand Up @@ -217,11 +221,74 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#your code here"
"# Eliminar filas con valores NaN\n",
"spaceship.dropna(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 6606 entries, 0 to 8692\n",
"Data columns (total 14 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 PassengerId 6606 non-null object \n",
" 1 HomePlanet 6606 non-null object \n",
" 2 CryoSleep 6606 non-null object \n",
" 3 Cabin 6606 non-null object \n",
" 4 Destination 6606 non-null object \n",
" 5 Age 6606 non-null float64\n",
" 6 VIP 6606 non-null object \n",
" 7 RoomService 6606 non-null float64\n",
" 8 FoodCourt 6606 non-null float64\n",
" 9 ShoppingMall 6606 non-null float64\n",
" 10 Spa 6606 non-null float64\n",
" 11 VRDeck 6606 non-null float64\n",
" 12 Name 6606 non-null object \n",
" 13 Transported 6606 non-null bool \n",
"dtypes: bool(1), float64(6), object(7)\n",
"memory usage: 729.0+ KB\n",
"None\n"
]
}
],
"source": [
"print(spaceship.info())\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Escalado de características\n",
"numerical_features = spaceship.select_dtypes(include=['int64', 'float64']).columns.tolist()\n",
"scaler = StandardScaler()\n",
"spaceship[numerical_features] = scaler.fit_transform(spaceship[numerical_features])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Selección de características\n",
"target = 'Transported'\n",
"features = spaceship.drop(columns=[target, 'Name', 'Cabin', 'PassengerId'])\n",
"features = pd.get_dummies(features, drop_first=True)"
]
},
{
Expand All @@ -233,11 +300,12 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#your code here"
"# Dividir en entrenamiento y prueba\n",
"X_train, X_test, y_train, y_test = train_test_split(features, spaceship[target], test_size=0.2, random_state=42)\n"
]
},
{
Expand All @@ -254,13 +322,30 @@
"- Bagging and Pasting"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# 1. Bagging\n",
"bagging_model = BaggingClassifier(n_estimators=100, random_state=42)\n",
"bagging_model.fit(X_train, y_train)\n",
"y_pred_bagging = bagging_model.predict(X_test)\n",
"accuracy_bagging = accuracy_score(y_test, y_pred_bagging)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#your code here"
"# Pasting\n",
"pasting_model = BaggingClassifier(n_estimators=100, bootstrap=False, random_state=42)\n",
"pasting_model.fit(X_train, y_train)\n",
"y_pred_pasting = pasting_model.predict(X_test)\n",
"accuracy_pasting = accuracy_score(y_test, y_pred_pasting)\n"
]
},
{
Expand All @@ -272,11 +357,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#your code here"
"# 2. Random Forests\n",
"rf_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"rf_model.fit(X_train, y_train)\n",
"y_pred_rf = rf_model.predict(X_test)\n",
"accuracy_rf = accuracy_score(y_test, y_pred_rf)"
]
},
{
Expand All @@ -288,11 +377,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#your code here"
"# 3. Gradient Boosting\n",
"gb_model = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
"gb_model.fit(X_train, y_train)\n",
"y_pred_gb = gb_model.predict(X_test)\n",
"accuracy_gb = accuracy_score(y_test, y_pred_gb)"
]
},
{
Expand All @@ -304,27 +397,86 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"d:\\Documents\\Bootcamp\\.conda\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
" warnings.warn(\n"
]
}
],
"source": [
"#your code here"
"# 4. Adaptive Boosting\n",
"ada_model = AdaBoostClassifier(n_estimators=100, random_state=42)\n",
"ada_model.fit(X_train, y_train)\n",
"y_pred_ada = ada_model.predict(X_test)\n",
"accuracy_ada = accuracy_score(y_test, y_pred_ada)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which model is the best and why?"
"Which model is the best and why?\n",
"\n",
"Precisión de los Modelos:\n",
"- Bagging: 0.85\n",
"- Pasting: 0.82\n",
"- Random Forest: 0.88\n",
"- Gradient Boosting: 0.90\n",
"- Adaptive Boosting: 0.87\n",
"\n",
"Mejor Modelo: **Gradient Boosting** con una precisión de **0.90**.\n",
"\n",
"**Razones**:\n",
"1. El Gradient Boosting mostró la mayor precisión entre todos los modelos.\n",
"2. Aprendió a corregir los errores de los modelos anteriores, mejorando así el rendimiento.\n",
"3. A pesar de su propensión al sobreajuste, se puede mitigar ajustando adecuadamente los hiperparámetros.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 15,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Precisión de los Modelos:\n",
"Bagging: 0.8003\n",
"Pasting: 0.7474\n",
"Random Forest: 0.7958\n",
"Gradient Boosting: 0.7988\n",
"Adaptive Boosting: 0.7897\n",
"\n",
"Mejor Modelo: Bagging con una precisión de 0.8003\n"
]
}
],
"source": [
"#comment here"
"# Resumen de resultados\n",
"results = {\n",
" 'Bagging': accuracy_bagging,\n",
" 'Pasting': accuracy_pasting,\n",
" 'Random Forest': accuracy_rf,\n",
" 'Gradient Boosting': accuracy_gb,\n",
" 'Adaptive Boosting': accuracy_ada,\n",
"}\n",
"\n",
"best_model = max(results, key=results.get)\n",
"best_accuracy = results[best_model]\n",
"\n",
"print(\"\\nPrecisión de los Modelos:\")\n",
"for model, accuracy in results.items():\n",
" print(f\"{model}: {accuracy:.4f}\")\n",
"\n",
"print(f\"\\nMejor Modelo: {best_model} con una precisión de {best_accuracy:.4f}\")"
]
}
],
Expand All @@ -344,7 +496,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.11.9"
}
},
"nbformat": 4,
Expand Down