Stacking Classifier
Tree Models & Ensembles DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding Ensemble Learning via Model Stacking, Base Estimators, Final Meta-Learner, Out-of-Fold Predictions, Stacking Generalization, Feature Transformation, Supervised Learning, Ensemble Methods, Model Evaluation, Statistical Learning, Computational Complexity, Stacking, Meta-Learning, Bias-Variance Tradeoff, Cross-Validation, Model Heterogeneity.
Implement a Stacking Classifier using Scikit Learn that combines a Random Forest, a Support Vector Machine, and a K Nearest Neighbors classifier as base estimators, with a Logistic Regression model as the final meta estimator. The function should accept training data (X train, y train) and test data (X test), fit the ensemble, and return the predictions for the test set.