Problem Statement and Metrics
Explore the fundamentals of ad click prediction by defining the problem statement and understanding key evaluation metrics. Learn how to handle imbalanced CTR data, design effective metrics like Normalized Cross-Entropy, and consider requirements such as latency and retraining frequency to optimize ad performance in production systems.
Ad click prediction
1. Problem statement
Predicting whether a user will click on an ad is a critical problem in digital advertising. It impacts everything—from revenue to user experience. Build a machine learning model that predicts whether an ad will be clicked.
We’re focusing on a binary classification problem:
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Input: Features about the user, the ad, and the context
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Output: Probability that the user will click the ad (1 for click, 0 for no click)
To keep things simple, we’ll not dive into the more complex multi-stage ad ranking pipelines (like cascaded classifiers). Instead, ...