Author(s): Amit Chauhan
Originally published on Towards AI.
Reducing input features technique for predictive modeling
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Photo by Alex Chumak on Unsplash
Feature importance is a technique to know the importance of input features based on some coefficient values. This technique might be helpful in large dimension datasets where sometimes we need to remove input features based on correlation or with dimensional reduction techniques.
Section 1: Introduction of feature importance
Section 2: Synthetic data generation
Section 3: Impurity mean decrease based feature importance
Section 4: Feature importance based on permutation
This article will help the machine learning learners who tend to learn more about the topics in machine learning.
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Published via Towards AI