DataPredict

API Reference - Others - ModelDatasetCreator

Modifies existing dataset so that it can be used by the models.

Creates a ModelDatasetCreator object.

ModelDatasetCreator.new(): ModelDatasetCreatorObject

Set the split percentages for training, validation and testing. The current default values are 0.7 for training and 0.3 for testing.

ModelDatasetCreator:setDatasetSplitPercentages(trainDataPercentage: number, validationDataPercentage: number, testDataPercentage: number)

trainDataPercentage: The percentage of dataset to be turned to training data. The value must be between 0 and 1.
validationDataPercentage: The percentage of dataset to be turned to validation data. The value must be between 0 and 1.
testDataPercentage: The percentage of dataset to be turned to testing data. The value must be between 0 and 1.

ModelDatasetCreator:setDatasetRandomizationProbability(datasetRandomizationProbability: number)

datasetRandomizationProbability: The probability to randomize the positions of each datapoints. The higher the value, the more likely it will be randomized. The value must be between 0 and 1.

Randomizes the each data positions in te dataset

ModelDatasetCreator:randomizeDataset(featureMatrix: matrix, labelVectorOrMatrix: matrix): matrix, matrix

featureMatrix: The matrix containing all the data.
labelVectorOrMatrix: The matrix containing all the label values related to feature matrix. Optional argument.

randomizedFeatureMatrix: The matrix containing all the data.
randomizedLabelVectorOrMatrix: The matrix containing all the label values related to feature matrix. Only returns if labelVectorOrMatrix is added.

Predict the values for given data.

ModelDatasetCreator:splitDataset(datasetMatrix): matrix, matrix, matrix