Build and evaluate click prediction models

Q: Build and evaluate click prediction models

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Question

Click-Through Rate (CTR) Prediction: Build, Compare, and Justify Models

Context

You are given a tabular dataset for binary click prediction (click = 1, no click = 0). The goal is to produce well-calibrated click probabilities for ranking/decisioning. Assume features include user, content/ad, and context signals (e.g., user/device attributes, ad/category IDs, time features, historical interaction counts). The class distribution is roughly balanced (e.g., 40–60% positives).

Task

Establish a trivial baseline classifier.
Train and compare three models: logistic regression, random forest, and gradient-boosted trees.
Explain why you selected each algorithm and why you did not choose plausible alternatives (e.g., SVM, Naive Bayes, simple neural networks), discussing bias–variance, interpretability, and computational trade-offs.
Describe your cross-validation strategy and the key hyperparameters you would tune for each model.
Choose evaluation metrics appropriate for a roughly class-balanced dataset (e.g., ROC AUC, log loss, PR AUC, accuracy), justify them, and explain what would change if classes were imbalanced.
Outline additional improvements you would pursue with more time (features, modeling, calibration, serving/monitoring).

Build and evaluate click prediction models

Click-Through Rate (CTR) Prediction: Build, Compare, and Justify Models

Context

Task

Solution (Locked)

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