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Overview

MinMaxScaler transforms features by scaling each feature to a given range. The default range is [0, 1].

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Constructor

new MinMaxScaler(options?: MinMaxScalerOptions)
​
options.featureRange
[number, number]
default:"[0, 1]"
Desired range of transformed data as [min, max]. Both values must be finite and min must be less than max.

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Properties

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dataMin_
Vector | null
Per feature minimum seen in the training data.
​
dataMax_
Vector | null
Per feature maximum seen in the training data.
​
dataRange_
Vector | null
Per feature range (dataMax_ - dataMin_) in the training data.
​
scale_
Vector | null
Per feature scaling factor.
​
min_
Vector | null
Per feature minimum offset.

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Methods

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fit

fit(X: Matrix): this
Compute the minimum and maximum to be used for later scaling.
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X
Matrix
required
Training data matrix where each row is a sample and each column is a feature.
Returns: this - The fitted scaler instance.

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transform

transform(X: Matrix): Matrix
Scale features of X according to feature_range.
​
X
Matrix
required
Data matrix to transform.
Returns: Matrix - Transformed data scaled to the specified range.

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fitTransform

fitTransform(X: Matrix): Matrix
Fit to data, then transform it. Equivalent to calling fit(X).transform(X).
​
X
Matrix
required
Training data matrix to fit and transform.
Returns: Matrix - Transformed data.

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inverseTransform

inverseTransform(X: Matrix): Matrix
Undo the scaling of X according to feature_range.
​
X
Matrix
required
Transformed data matrix to inverse transform.
Returns: Matrix - Original scale data.

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Example

import { MinMaxScaler } from 'bun-scikit';

// Training data
const X = [
  [1, 2],
  [2, 4],
  [3, 6],
  [4, 8]
];

// Scale to [0, 1] (default)
const scaler = new MinMaxScaler();
const XScaled = scaler.fitTransform(X);
console.log('Scaled [0,1]:', XScaled);
// Output: Scaled [0,1]: [[0, 0], [0.333, 0.333], [0.667, 0.667], [1, 1]]

// Scale to custom range [-1, 1]
const scaler2 = new MinMaxScaler({ featureRange: [-1, 1] });
const XScaled2 = scaler2.fitTransform(X);
console.log('Scaled [-1,1]:', XScaled2);
// Output: Scaled [-1,1]: [[-1, -1], [-0.333, -0.333], [0.333, 0.333], [1, 1]]

// Check fitted parameters
console.log('Data min:', scaler.dataMin_);
// Output: Data min: [1, 2]
console.log('Data max:', scaler.dataMax_);
// Output: Data max: [4, 8]

// Inverse transform
const XOriginal = scaler.inverseTransform(XScaled);
console.log('Original:', XOriginal);
// Output: Original: [[1, 2], [2, 4], [3, 6], [4, 8]]

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Notes

  • Features with zero range (constant values) are not scaled
  • The scaler must be fitted before calling transform() or inverseTransform()
  • Input data must be finite (no NaN or Infinity values)
  • Useful when features need to be in a specific bounded range

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