As you saw when running the trainer component of the sample project, there are various elements of model evaluation. For each model type, there are different metrics to look at when analyzing the performance of a model.
In binary classification models like the one found in the example project, the following properties are exposed in CalibratedBiniaryClassificationMetrics that we set after calling the Evaluate method. However, first, we need to define the four prediction types in a binary classification:
- True negative: Properly classified as negative
- True positive: Properly classified as positive
- False negative: Improperly classified as negative
- False positive: Improperly classified as positive
The first metric to understand is Accuracy. As the name implies, accuracy is one of the most commonly used metrics when evaluating a model. This metric is calculated simply as the ratio of correctly classified predictions to total classifications.
The next metric to understand...
