Feature Importance of Data in Machine Learning with Python

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