Teaching a Random Forest to Tell Walking from Running: A Computer Vision Pipeline with Hand-Built...

1. 1Build a baseline random forest classifier with 56 features
2. 2Use per-class SHAP to guide feature engineering decisions
3. 3Select and engineer additional features to improve classifier accuracy
4. 4Train and test the updated classifier with 240 features
5. 5Evaluate the performance of the classifier using accuracy metrics

Data scientists and machine learning engineers can benefit from this article to improve their feature engineering skills and build more accurate classifiers

💡 Using per-class SHAP to guide feature engineering decisions can significantly improve the accuracy of a random forest classifier

🚀 Boost classifier accuracy from 56 to 240 features with per-class SHAP! 💡