How to Calibrate Probabilities for Imbalanced Classification
Last Updated on August 21, 2020 Many machine learning models are capable of predicting a probability or probability-like scores for class membership. Probabilities provide a required level of granularity for evaluating and comparing models, especially on imbalanced classification problems where tools like ROC Curves are used to interpret predictions and the ROC AUC metric is used to compare model performance, both of which use probabilities. Unfortunately, the probabilities or probability-like scores predicted by many models are not calibrated. This means […]
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