WeightedConfusionMatrix Class

Creates a new Confusion Matrix.

Creates a new Confusion Matrix.

Creates a new Confusion Matrix.

Accuracy. This is the same value as OverallAgreement.

Chance agreement.

Gets the Chi-Square statistic for the contingency table.

Obsolete. Please use NumberOfClasses instead.

Gets the col errors.

Gets the column marginals (proportions).

Gets the column totals.

Cramer's V association measure.

Gets the diagonal of the confusion matrix.

Error. This is the same value as 1.0 - OverallAgreement.

Expected values, or values that could have been generated just by chance.

Geometric agreement.

Gets the Kappa coefficient of performance.

Gets the confusion matrix, in which each element e_ij represents the number of elements from class i classified as belonging to class j.

Gets the maximum number of correct matches (the maximum over the diagonal)

Gets the minimum number of correct matches (the minimum over the diagonal)

Gets the number of classes.

Gets the number of samples.

Overall agreement.

Pearson's contingency coefficient C.

Gets binary confusion matrices for each class in the multi-class classification problem. You can use this property to obtain recall, precision and other metrics for each of the classes.

Phi coefficient.

Gets the row precision.

Gets the confusion matrix in terms of cell percentages.

Gets the column recall.

Gets the row errors.

Gets the row marginals (proportions).

Gets the row totals.

Sakoda's contingency coefficient V.

Obsolete. Please use NumberOfSamples instead.

Gets the standard error of the Kappa coefficient of performance.

Gets the standard error of the Kappa under the null hypothesis that the underlying Kappa value is 0.

Gets the Tau coefficient of performance.

Gets or sets the title that ought be displayed on top of the columns of this GeneralConfusionMatrix. Default is "Expected (Ground-truth)".

Gets or sets the title that ought be displayed on left side of this GeneralConfusionMatrix. Default is "Actual (Prediction)".

Tschuprow's T association measure.

Gets the variance of the Kappa coefficient of performance.

Gets the variance of the Kappa coefficient of performance using Congalton's delta method.

Gets the variance of the Kappa under the null hypothesis that the underlying Kappa value is 0.

Chance agreement.

Gets the column marginals (proportions).

Gets the Kappa coefficient of performance.

Overall agreement.

Gets the row marginals (proportions).

Gets the standard error of the WeightedKappa coefficient of performance.

Gets the standard error of the WeightedKappa under the null hypothesis that the underlying Kappa value is 0.

Gets the variance of the WeightedKappa coefficient of performance.

Gets the variance of the WeightedKappa under the null hypothesis that the underlying Kappa value is 0.

Gets the Weights matrix.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as the default hash function.

Gets the Type of the current instance.

Creates a new Weighted Confusion Matrix with linear weighting.

Creates a shallow copy of the current Object.

Creates a new Weighted Confusion Matrix with linear weighting.

Returns a string that represents the current object.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.