Handle imbalance, sampling, and overfitting

Q: Handle imbalance, sampling, and overfitting

This question evaluates knowledge of handling class imbalance, verifying sample representativeness and generalization, preventing overfitting in tree-based models, and understanding the bias implications of L1/L2 regularization.

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Question

You are asked several machine learning fundamentals questions:

You are building a binary classifier with a highly imbalanced target . How would you handle the imbalance during training, and how would you evaluate the model?
The full dataset is too large to train on directly , so you train using a sample. How would you verify that the sample is representative of the full dataset and that the resulting model generalizes well to the full population?
You are using a tree-based model . How would you prevent overfitting?
Why are L1- and L2-regularized estimators biased , and why can they still outperform an unbiased estimator on out-of-sample prediction?

Handle imbalance, sampling, and overfitting

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