What Are Ensemble Methods?
Ensemble is a collection of models whose predictions are combined to improve overall performance.
Ensemble methods combine predictions from multiple models to produce a final output that is often more accurate and robust than any single model alone. This approach leverages the strengths and compensates for the weaknesses of individual models.
Ensembles often provide more stable and generalizable predictions, especially on noisy or complex datasets.
When individual models (base learners) make different errors, combining them can reduce the overall error rate. This is sometimes called the "wisdom of the crowd" effect.
Base learner - an individual model (such as a DecisionTreeClassifier) used as a building block in an ensemble.
123456789101112131415161718192021222324252627from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import BaggingClassifier from sklearn.metrics import accuracy_score # Generate a toy dataset X, y = make_classification(n_samples=500, n_features=10, n_informative=5, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Single decision tree tree = DecisionTreeClassifier(random_state=42) tree.fit(X_train, y_train) tree_pred = tree.predict(X_test) tree_acc = accuracy_score(y_test, tree_pred) print(f"Decision Tree accuracy: {tree_acc:.2f}") # Bagging ensemble of decision trees bagging = BaggingClassifier( estimator=DecisionTreeClassifier(), n_estimators=30, random_state=42 ) bagging.fit(X_train, y_train) bagging_pred = bagging.predict(X_test) bagging_acc = accuracy_score(y_test, bagging_pred) print(f"Bagging Ensemble accuracy: {bagging_acc:.2f}")
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Ensemble is a collection of models whose predictions are combined to improve overall performance.
Ensemble methods combine predictions from multiple models to produce a final output that is often more accurate and robust than any single model alone. This approach leverages the strengths and compensates for the weaknesses of individual models.
Ensembles often provide more stable and generalizable predictions, especially on noisy or complex datasets.
When individual models (base learners) make different errors, combining them can reduce the overall error rate. This is sometimes called the "wisdom of the crowd" effect.
Base learner - an individual model (such as a DecisionTreeClassifier) used as a building block in an ensemble.
123456789101112131415161718192021222324252627from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import BaggingClassifier from sklearn.metrics import accuracy_score # Generate a toy dataset X, y = make_classification(n_samples=500, n_features=10, n_informative=5, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) # Single decision tree tree = DecisionTreeClassifier(random_state=42) tree.fit(X_train, y_train) tree_pred = tree.predict(X_test) tree_acc = accuracy_score(y_test, tree_pred) print(f"Decision Tree accuracy: {tree_acc:.2f}") # Bagging ensemble of decision trees bagging = BaggingClassifier( estimator=DecisionTreeClassifier(), n_estimators=30, random_state=42 ) bagging.fit(X_train, y_train) bagging_pred = bagging.predict(X_test) bagging_acc = accuracy_score(y_test, bagging_pred) print(f"Bagging Ensemble accuracy: {bagging_acc:.2f}")
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