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
Constants
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