Stacking Ensembles and Meta-Learners
Stacking is an advanced ensemble technique where predictions from multiple base models are used as input features for a higher-level model, called a meta-learner. The meta-learner learns how to best combine the base models' outputs to improve overall performance.
Meta-learner is a model that takes the predictions of base models as input and learns how to combine them for the final prediction.
Stacking generalization is the process of training a meta-learner on the outputs of base models to generalize better than any single model.
1234567891011121314151617181920212223242526272829303132from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import StackingClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC # Load dataset X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) # Define base models base_estimators = [ ('dt', DecisionTreeClassifier(random_state=42)), ('lr', LogisticRegression(max_iter=1000, random_state=42)), ('svc', SVC(probability=True, random_state=42)) ] # Define meta-learner meta_learner = LogisticRegression(max_iter=1000, random_state=42) # Build stacking ensemble stacking_clf = StackingClassifier( estimators=base_estimators, final_estimator=meta_learner, cv=5 ) # Train and evaluate stacking_clf.fit(X_train, y_train) score = stacking_clf.score(X_test, y_test) print(f"Stacking ensemble accuracy: {score:.2f}")
In the code above, the base models generate predictions that are then used by the meta-learner to make the final decision. This approach can capture patterns that individual models might miss.
Danke für Ihr Feedback!
Fragen Sie AI
Fragen Sie AI
Fragen Sie alles oder probieren Sie eine der vorgeschlagenen Fragen, um unser Gespräch zu beginnen
Can you explain how stacking differs from other ensemble methods like bagging or boosting?
What are the advantages and disadvantages of using stacking?
Can you suggest scenarios where stacking would be especially beneficial?
Großartig!
Completion Rate verbessert auf 7.14Stacking Ensembles and Meta-Learners
Swipe um das Menü anzuzeigen
Stacking is an advanced ensemble technique where predictions from multiple base models are used as input features for a higher-level model, called a meta-learner. The meta-learner learns how to best combine the base models' outputs to improve overall performance.
Meta-learner is a model that takes the predictions of base models as input and learns how to combine them for the final prediction.
Stacking generalization is the process of training a meta-learner on the outputs of base models to generalize better than any single model.
1234567891011121314151617181920212223242526272829303132from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import StackingClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC # Load dataset X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42) # Define base models base_estimators = [ ('dt', DecisionTreeClassifier(random_state=42)), ('lr', LogisticRegression(max_iter=1000, random_state=42)), ('svc', SVC(probability=True, random_state=42)) ] # Define meta-learner meta_learner = LogisticRegression(max_iter=1000, random_state=42) # Build stacking ensemble stacking_clf = StackingClassifier( estimators=base_estimators, final_estimator=meta_learner, cv=5 ) # Train and evaluate stacking_clf.fit(X_train, y_train) score = stacking_clf.score(X_test, y_test) print(f"Stacking ensemble accuracy: {score:.2f}")
In the code above, the base models generate predictions that are then used by the meta-learner to make the final decision. This approach can capture patterns that individual models might miss.
Danke für Ihr Feedback!
