Train with imbalanced sampled data

Q: Train with imbalanced sampled data

This question evaluates competency in handling class imbalance, designing representative sampling strategies, verifying sample-to-population generalization, preventing overfitting in tree-based models, and selecting evaluation metrics for highly imbalanced binary classification.

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Question

You are training a binary classifier on a very large dataset where the positive class is rare. Because the full dataset is too large to train on directly, you plan to draw a sample and train a tree-based model.

Explain how you would:

Handle class imbalance during training.
Verify that the sampled training data is representative of the full population.
Validate that a model trained on the sample generalizes to the full dataset.
Prevent overfitting in a tree-based model.
Choose evaluation metrics, especially when the classes are highly imbalanced.

Train with imbalanced sampled data

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