One of the default evaluation libraries in a modeling objective is the binary classification evaluator. This library provides a core set of commonly used metrics for evaluating binary classification models.
The below metrics are produced for every subset bucket configured in the evaluation dashboard. It may not be possible to generate all metrics on every subset if, for example, the generated subset does not contain both classes in the model prediction or label columns.
The default binary classification evaluator produces the following numeric metrics:
The default binary classification evaluator produces the following plots:
0.05
, which results in 20 total buckets in the [0.0, 1.0]
range.For full configuration instructions, see the documentation on how to configure a model evaluation library.
The following fields are required for a binary classification evaluator. The expected value type for these columns is integer.
1
is the positive class and a 0
is the negative class.1
is the positive class and a 0
is the negative class.probability_field: This is an optional field that represents the probability of a positive prediction class. When the probability_field is provided, the default binary classification evaluation library will produce the following metrics:
max_samples_for_roc The maximum number of samples to generate the model ROC curve on. If not provided, this will default to 200
.