Contenuti del Corso

Ensemble Learning

1. Basic Principles of Building Ensemble Models

What is Ensemble of Models?Bagging Models Boosting Models Stacking Models

2. Commonly Used Bagging Models

Bagging Classifier Challenge: Solving Task Using Bagging Classifier Bagging Regressor Challenge: Solving Task Using Bagging Regressor Random Forest Challenge: Determining Feature Importances Using Random Forest ExtraTrees

3. Commonly Used Boosting Models

AdaBoost Classifier Challenge: Solving Task Using AdaBoost Classifier Challenge: Solving Task Using AdaBoost Regressor Gradient Boosting XGBoost Challenge: Solving Task Using XGBoost

4. Commonly Used Stacking Models

Stacking Classifier Challenge: Solving Task Using Stacking Classifier Challenge: Solving Task Using Stacking Regressor Using Ensembles As Base Models Course Summary

Stacking Classifier

Stacking Classifier is a stacking ensemble model which is used to solve classification tasks. It aims to exploit the strengths of individual models by using their predictions as input for a higher-level model, known as the meta-classifier or second-level model. The meta-classifier learns how to combine the predictions from the base models to make the final classification decision.

How does Stacking Classifier work?

Base Models: Several different classification models are trained independently on the training data. These diverse models can utilize various algorithms, architectures, or parameter settings;
Prediction Generation: After training the base models, they are used to make predictions on both the training data. These predictions serve as features (meta-features) for the next level of modeling;
Meta-Classifier: A higher-level classifier (meta-classifier) is trained using the meta-features generated from the base models. The meta-classifier learns to combine the base model predictions to make a final classification decision;
Final Prediction: The base models generate predictions for the new input data during prediction. These predictions are then used as input features for the meta-classifier, which produces the final classification prediction.

Example


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import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import StackingClassifier
from sklearn.metrics import f1_score
import warnings

warnings.filterwarnings('ignore')


# Load the Breast Cancer dataset
data = load_breast_cancer()
X = data.data
y = data.target

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define base models
base_models = []
for i in range(5):  # Create 5 different Decision Tree models
    base_models.append(('decision_tree_' + str(i), DecisionTreeClassifier()))

for i in range(3):  # Create 3 different SVM models
    base_models.append(('svm_' + str(i), SVC(probability=True)))

# Define meta-classifier
meta_classifier = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=200)

# Create the stacking ensemble
stacking_classifier = StackingClassifier(estimators=base_models, final_estimator=meta_classifier)

# Train the stacking classifier
stacking_classifier.fit(X_train, y_train)

# Make predictions
y_pred = stacking_classifier.predict(X_test)

# Calculate F1 score
f1 = f1_score(y_test, y_pred, average='weighted')
print(f'F1 Score: {f1:.4f}')

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Sezione 4. Capitolo 1

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