Maintaining Feature Balance in Machine Learning ModelsFeature or column importance in ML models gauges predictor significance.
Understanding Explainability & Prediction DetailsEntity-level explainability is a great tool for understanding and interpreting ML models, improving them and even help finding errors.
SHAP valuesSHAP values quantify feature impact in ML models, revealing key drivers in predictions and aiding in data-driven decision-making.
Model performance metrics for binary modelsLearn about binary model metrics: Base Rate, Precision, Detection, AUC, LogLoss guide accurate, balanced predictions for distinct classes.