A Gentle Introduction to Probability Metrics for Imbalanced Classification
Last Updated on January 14, 2020
Classification predictive modeling involves predicting a class label for examples, although some problems require the prediction of a probability of class membership.
For these problems, the crisp class labels are not required, and instead, the likelihood that each example belonging to each class is required and later interpreted. As such, small relative probabilities can carry a lot of meaning and specialized metrics are required to quantify the predicted probabilities.
In this tutorial, you will discover metrics for evaluating probabilistic predictions for imbalanced classification.
After completing this tutorial, you will know:
- Probability predictions are required for some classification predictive modeling problems.
- Log loss quantifies the average difference between predicted and expected probability distributions.
- Brier score quantifies the average difference between predicted and expected probabilities.
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