AggregateClassificationMetrics class
Aggregate metrics for classification/classifier models.
For multi-class models, the metrics are either macro-averaged or micro-averaged. When macro-averaged, the metrics are calculated for each label and then an unweighted average is taken of those values. When micro-averaged, the metric is calculated globally by counting the total number of correctly predicted rows.
Constructors
- AggregateClassificationMetrics({double? accuracy, double? f1Score, double? logLoss, double? precision, double? recall, double? rocAuc, double? threshold})
- AggregateClassificationMetrics.fromJson(Map json_)
Properties
- accuracy ↔ double?
-
Accuracy is the fraction of predictions given the correct label.
getter/setter pair
- f1Score ↔ double?
-
The F1 score is an average of recall and precision.
getter/setter pair
- hashCode → int
-
The hash code for this object.
no setterinherited
- logLoss ↔ double?
-
Logarithmic Loss.
getter/setter pair
- precision ↔ double?
-
Precision is the fraction of actual positive predictions that had positive
actual labels.
getter/setter pair
- recall ↔ double?
-
Recall is the fraction of actual positive labels that were given a
positive prediction.
getter/setter pair
- rocAuc ↔ double?
-
Area Under a ROC Curve.
getter/setter pair
- runtimeType → Type
-
A representation of the runtime type of the object.
no setterinherited
- threshold ↔ double?
-
Threshold at which the metrics are computed.
getter/setter pair
Methods
-
noSuchMethod(
Invocation invocation) → dynamic -
Invoked when a nonexistent method or property is accessed.
inherited
-
toJson(
) → Map< String, dynamic> -
toString(
) → String -
A string representation of this object.
inherited
Operators
-
operator ==(
Object other) → bool -
The equality operator.
inherited