MulticlassClassificationMetrics Class
Definition
Important
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Evaluation results for multiclass 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 logloss of the classifier. Logloss measures the performance of a classifier with respect to how much the predicted probabilities diverge from the true class label. Lower logloss indicates a better model. A perfect model, which predicts a probability of 1 for the true class, will have a logloss of 0. 
LogLossReduction 
Gets the logloss reduction (also known as relative logloss, 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. Logloss reduction closer to 1 indicates a better model. 
MacroAccuracy 
Gets the macroaverage accuracy of the model. 
MicroAccuracy 
Gets the microaverage accuracy of the model. 
PerClassLogLoss 
Gets the logloss of the classifier for each class. Logloss measures the performance of a classifier with respect to how much the predicted probabilities diverge from the true class label. Lower logloss indicates a better model. A perfect model, which predicts a probability of 1 for the true class, will have a logloss 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
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