January 2025
Intermediate to advanced
436 pages
11h 10m
English
Content preview from Feature Engineering for Modern Machine Learning with Scikit-LearnBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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2.4 What Could Go Wrong?
Feature engineering is crucial for creating effective predictive models, yet several challenges and pitfalls can arise. Below are some common issues to be aware of, along with suggestions to mitigate these potential problems.
2.4.1 Overfitting Due to Complex Features
When creating complex features that capture too much specific detail, it can lead to overfitting, where the model performs well on training data but poorly on unseen data. For example, overly granular features based on specific time windows or highly detailed behavior patterns may not generalize well.
What could go wrong?
- Models may fail to generalize and exhibit poor performance on test or real-world data.
- Overfit models can be unreliable, as they capture noise ...
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