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Jun 29, 2020 · The feature importance (variable importance) describes which features are relevant. It can help with better understanding of the solved problem and sometimes lead to model improvements by employing the feature selection. In this post, I will present 3 ways (with code examples) how to compute feature importance for the Random Forest algorithm from scikit-learn package (in Python). Basic Random Forest Model by Trey Causey. Minimally commented but clear code for using Pandas and scikit-learn to analyze in-game NFL win probabilities. Supervised Learning In-Depth: SVMs and Random Forests by Jake Vanderplas; Text Classification with Naïve Bayes by Guillermo Moncecchi. Python code from the second chapter of Learning scikit ... Blog: Random Forest Classifier Example by Chris Albon. This tutorial is based on Yhat’s 2013 tutorial on Random Forests in Python. - NoteBook; NoteBook: Titanic Competition with Random Forest by Chris Albon ; Infographic and Code: Decision Trees (100 Days Of ML Code) by Avik Jain