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Overview

StandardScaler transforms features to have zero mean and unit variance. This is a common requirement for many machine learning algorithms.

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Constructor

new StandardScaler()
No parameters required.

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Properties

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mean_
Vector | null
Mean value for each feature in the training set. Set during fit().
​
scale_
Vector | null
Standard deviation for each feature in the training set. Set during fit().

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Methods

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fit

fit(X: Matrix): this
Compute the mean and standard deviation 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
Perform standardization by centering and scaling.
​
X
Matrix
required
Data matrix to transform.
Returns: Matrix - Transformed data with zero mean and unit variance.

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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
Scale back the data to the original representation.
​
X
Matrix
required
Transformed data matrix to inverse transform.
Returns: Matrix - Original scale data.

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Example

import { StandardScaler } from 'bun-scikit';

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

// Create and fit scaler
const scaler = new StandardScaler();
scaler.fit(X);

console.log('Mean:', scaler.mean_);
// Output: Mean: [2.5, 5]

console.log('Scale:', scaler.scale_);
// Output: Scale: [1.118..., 2.236...]

// Transform data
const XScaled = scaler.transform(X);
console.log('Scaled:', XScaled);
// Output: Scaled: [[-1.34, -1.34], [-0.45, -0.45], [0.45, 0.45], [1.34, 1.34]]

// Or use fit_transform pattern
const scaler2 = new StandardScaler();
const XScaled2 = scaler2.fitTransform(X);

// 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 variance are scaled to 1 to avoid division by zero
  • The scaler must be fitted before calling transform() or inverseTransform()
  • Input data must be finite (no NaN or Infinity values)

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