Standardizer class abstract

A class that performs data standardization.

Data standardization is a process, targeted to make the data look like normally distributed data (with zero mean and unit variance).

Standardization applies column-wise.

Example:

import 'package:ml_dataframe/ml_dataframe.dart';
import 'package:ml_preprocessing/ml_preprocessing.dart';

void main() {
  final data = DataFrame([
    ['feature_1', 'feature_2', 'label'],
    [         10,        33.2,       2],
    [         20,          -1,       4],
    [         40,         -10,       5],
    [         55,         100,      10],
  ]);
  final standardizer = Standardizer(data);
  final processed = standardizer.process(data);

  print(processed);
  // DataFrame (4 x 3)
  //           feature_1             feature_2                  label
  //  -1.217395305633545   0.06132180616259575    -1.1026456356048584
  // -0.6445034146308899   -0.7300761342048645   -0.42409446835517883
  //  0.5012804269790649   -0.9383387565612793   -0.08481889218091965
  //  1.3606183528900146     1.607093095779419     1.6115589141845703
}

Constructors

Standardizer(DataFrame fittingData, {DType dtype})

factory

Properties

hashCode → int

The hash code for this object.

no setter, inherited

runtimeType → Type

A representation of the runtime type of the object.

no setter, inherited

Methods

noSuchMethod(Invocation invocation) → dynamic

Invoked when a nonexistent method or property is accessed.

inherited

process(DataFrame input) → DataFrame

inherited

toString() → String

A string representation of this object.

inherited

Operators

operator ==(Object other) → bool

The equality operator.

inherited