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MulticlassClassificationMetrics Class

Definition

Evaluation results for multi-class classification trainers.

public sealed class MulticlassClassificationMetrics
type MulticlassClassificationMetrics = class
Public NotInheritable Class MulticlassClassificationMetrics
Inheritance
MulticlassClassificationMetrics

Properties

ConfusionMatrix

The confusion matrix giving the counts of the predicted classes versus the actual classes.

LogLoss

Gets the average log-loss of the classifier. Log-loss measures the performance of a classifier with respect to how much the predicted probabilities diverge from the true class label. Lower log-loss indicates a better model. A perfect model, which predicts a probability of 1 for the true class, will have a log-loss of 0.

LogLossReduction

Gets the log-loss reduction (also known as relative log-loss, or reduction in information gain - RIG) of the classifier. It gives a measure of how much a model improves on a model that gives random predictions. Log-loss reduction closer to 1 indicates a better model.

MacroAccuracy

Gets the macro-average accuracy of the model.

MicroAccuracy

Gets the micro-average accuracy of the model.

PerClassLogLoss

Gets the log-loss of the classifier for each class. Log-loss measures the performance of a classifier with respect to how much the predicted probabilities diverge from the true class label. Lower log-loss indicates a better model. A perfect model, which predicts a probability of 1 for the true class, will have a log-loss of 0.

TopKAccuracy

Convenience method for "TopKAccuracyForAllK[TopKPredictionCount - 1]". If TopKPredictionCount is positive, this is the relative number of examples where the true label is one of the top K predicted labels by the predictor.

TopKAccuracyForAllK

Returns the top K accuracy for all K from 1 to the value of TopKPredictionCount.

TopKPredictionCount

If positive, this indicates the K in TopKAccuracy and TopKAccuracyForAllK.

Applies to