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 values to guide feature engineering decisions
3. 3Engineer new features based on SHAP values to improve model performance
4. 4Configure and train the updated Random Forest classifier with 240 features
5. 5Test and evaluate the performance of the updated model using accuracy metrics

Data scientists and machine learning engineers can benefit from this article to improve their model's performance and understand the importance of feature engineering in computer vision pipelines

💡 Per-class SHAP values can be used to guide feature engineering decisions and significantly improve the performance of a Random Forest classifier

🚀 Improve your Random Forest classifier's accuracy with per-class SHAP values! 📈 From 56 features to 240 features with 86% accuracy 🤯