A machine learning engineer has developed a single, deep decision tree model to predict customer churn. The model achieves near-perfect accuracy on the training data, but its performance on a held-out validation set is significantly worse. This suggests the model is overfitting. The engineer decides to implement bootstrap aggregation (bagging) using this type of deep decision tree as the base estimator. What is the primary mechanism by which bagging is expected to improve the model's performance on unseen data in this scenario?
By systematically creating bootstrap samples, the bagging process inherently identifies and gives more weight to the most important predictive features.
By simplifying the model structure through aggregation, the overall computational cost and inference time are significantly decreased compared to the single complex tree.
By combining multiple models, the ensemble is able to correct the inherent systematic errors (bias) of the individual deep decision trees.
By training multiple versions of the high-variance base estimator on different bootstrap samples and averaging their predictions, the variance of the final ensemble model is reduced.
The correct answer explains that bagging improves performance by reducing variance. The scenario describes a classic case of a model with high variance and low bias (a deep decision tree that overfits). Bagging trains multiple independent models on different random subsets of the data (bootstrap samples) and then averages their predictions. This process of aggregation effectively smooths out the predictions and reduces the overall variance of the ensemble, making it less sensitive to the noise in any single training set and thus improving its generalization to unseen data.
The option regarding bias reduction is incorrect. Bagging's primary effect is on variance, not bias. While boosting techniques are designed to sequentially reduce bias, bagging averages models and does not systematically correct for bias. If the base model is biased, the bagged model will likely retain that bias.
The option regarding feature importance is incorrect. Standard bagging does not inherently perform feature selection or weighting. This is a characteristic of Random Forest, which is a specific type of bagging that adds another layer of randomness by selecting a subset of features at each split.
The option regarding decreased computational cost is incorrect. Bagging increases computational complexity because it requires training multiple models instead of just one. While the training of individual models can be parallelized, the total computational cost is higher than that of a single model.
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