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

1. 1Build a baseline model with 56 features
2. 2Use per-class SHAP to guide feature engineering decisions
3. 3Engineer new features to improve model accuracy
4. 4Train a random forest classifier with 240 features
5. 5Evaluate the model's performance 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 your classifier's accuracy to 86% with per-class SHAP guided feature engineering! 📈