The confusion matrix serves as a tool for assessing a model’s misclassifications. It is generated by comparing the actual labels with the predicted labels.
Creates a ModelDatasetCreator object.
ConfusionMatrixCreator.new(ClassesList: []): ConfusionMatrixCreatorObject
ClassesList: A list of classes. The index of the class relates to which the neuron at output layer belong to. For example, {3, 1} means that the output for 3 is at first neuron, and the output for 1 is at second neuron.
ConfusionMatrixCreator:setParameters(ClassesList: [])
ClassesList: A list of classes. The index of the class relates to which the neuron at output layer belong to. For example, {3, 1} means that the output for 3 is at first neuron, and the output for 1 is at second neuron.
Creates a confusion matrix. The rows represent actual classes, columns represent predicted classes.
ConfusionMatrixCreator:createConfusionMatrix(trueLabelVector: matrix, predictedLabelVector: matrix): matrix
trueLabelVector: The rows represent actual classes and the columns represent predicted classes.
predictedLabelVector: The vector containing all the predicted classes generated by a model.
Prints out a confusion matrix. The rows represent actual classes and the columns represent predicted classes.
ConfusionMatrixCreator:printConfusionMatrix(trueLabelVector: matrix, predictedLabelVector: matrix): matrix
trueLabelVector: The vector containing all the true classes.
predictedLabelVector: The vector containing all the predicted classes generated by a model.