Package com.datumbox.framework.core.machinelearning.preprocessing

Class Summary

Class	Description
BinaryScaler	Rescales the numerical features of the dataset between -1 and 1.
BinaryScaler.ModelParameters	Base class for every ModelParameter class in the framework.
BinaryScaler.TrainingParameters	The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.
CornerConstraintsEncoder	Encodes the categorical columns of the dataset into booleans using the Corner Constraints encoding (also known as set-to-zero).
CornerConstraintsEncoder.ModelParameters	Base class for every ModelParameter class in the framework.
CornerConstraintsEncoder.TrainingParameters	The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.
MaxAbsScaler	Rescales the numerical features of the dataset between -1 and 1.
MaxAbsScaler.ModelParameters	Base class for every ModelParameter class in the framework.
MaxAbsScaler.TrainingParameters	The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.
MinMaxScaler	Rescales the numerical features of the dataset between 0 and 1.
MinMaxScaler.ModelParameters	Base class for every ModelParameter class in the framework.
MinMaxScaler.TrainingParameters	The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.
OneHotEncoder	Encodes the categorical columns of the dataset into booleans using the One Hot Encoding method.
OneHotEncoder.ModelParameters	Base class for every ModelParameter class in the framework.
OneHotEncoder.TrainingParameters	The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.
StandardScaler	Rescales the numerical features of the dataset by subtracting the mean and dividing by the standard deviation.
StandardScaler.ModelParameters	Base class for every ModelParameter class in the framework.
StandardScaler.TrainingParameters	The AbstractTrainingParameters class stores the parameters that can be changed before training the algorithm.