Ad Click Prediction Model
Explore how to design ad click prediction models with effective feature engineering, training data strategies, and model evaluation methods. Understand how to handle imbalanced data, select features like advertiser IDs and user behavior, and enhance model accuracy using deep learning techniques. By the end, you will grasp how to balance training time with performance and apply evaluation strategies to improve prediction quality.
We'll cover the following...
We'll cover the following...
3. Model
Feature engineering
| Features | Feature engineering | Description |
|---|---|---|
| AdvertiserID | Use Embedding or feature hashing | It’s easy to have millions of advertisers |
| User’s historical behavior, i.e., numbers of clicks on ads over a period of time. | Feature scaling, i.e., normalization | |
| Temporal: |