ml_preprocessing library

Classes

Encoder
Categorical data encoder factory.
Normalizer
A class that performs normalization of data.
Pipeline
A class that is used to organize data preprocessing stages in a pipeline manner.
Standardizer
A class that performs data standardization.

Enums

Norm
Vector norm types
UnknownValueHandlingType
A way to handle unknown categorical data

Functions

normalize([Norm norm = Norm.euclidean]) → PipeableOperatorFn
Returns a function that can be used in Pipeline. The function creates a Normalizer instance. Example:
standardize() → PipeableOperatorFn
Returns a function that can be used in Pipeline. The function creates a Standardizer instance. Example:
toIntegerLabels({Iterable<int>? columnIndices, Iterable<String>? columnNames, String headerPrefix = '', String headerPostfix = '', UnknownValueHandlingType unknownValueHandlingType = defaultUnknownValueHandlingType}) → PipeableOperatorFn
A factory function to use label categorical data encoder in the pipeline
toOneHotLabels({Iterable<int>? columnIndices, Iterable<String>? columnNames, String headerPrefix = '', String headerPostfix = '', UnknownValueHandlingType unknownValueHandlingType = defaultUnknownValueHandlingType}) → PipeableOperatorFn
A factory function to use one hot categorical data encoder in the pipeline