January 2025
Intermediate to advanced
436 pages
11h 10m
English
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4.4 What Could Go Wrong?
Feature selection and model tuning are powerful tools for optimizing machine learning models, but they come with potential challenges and risks. Here’s a look at common issues you may encounter with Recursive Feature Elimination (RFE), feature importance, and model tuning, along with strategies to mitigate these pitfalls.
4.4.1 Overfitting from Selecting Too Few or Too Many Features
A common issue with RFE and feature selection is overfitting due to either selecting too few features (leading to underfitting) or too many (leading to overfitting). Selecting too few features can strip away valuable information, while including too many can increase model complexity unnecessarily.
What could go wrong?
- With too few features, the ...
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