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Overview

LinearRegression fits a linear model with coefficients to minimize the residual sum of squares between the observed targets and the predictions. This implementation uses the normal equation method with native Zig acceleration.

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Constructor

import { LinearRegression } from "bun-scikit";

const model = new LinearRegression(options);

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Parameters

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options
LinearRegressionOptions
default:"{}"
Configuration options for the model

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Methods

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fit

Fit the linear model using training data. 
fit(X: Matrix, y: Vector, sampleWeight?: Vector): this
Parameters:
  • X: Training data matrix of shape [nSamples, nFeatures]
  • y: Target values vector of length nSamples
  • sampleWeight: Optional sample weights (not currently used)
Returns: The fitted model instance Example: 
const X = [[1], [2], [3], [4], [5]];
const y = [3, 5, 7, 9, 11]; // y = 2x + 1

const model = new LinearRegression({ solver: "normal" });
model.fit(X, y);

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predict

Predict using the linear model. 
predict(X: Matrix): Vector
Parameters:
  • X: Samples matrix of shape [nSamples, nFeatures]
Returns: Predicted values vector of length nSamples Example: 
const predictions = model.predict([[6]]);
console.log(predictions[0]); // ~13

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score

Return the coefficient of determination (R² score) of the prediction. 
score(X: Matrix, y: Vector): number
Parameters:
  • X: Test samples matrix
  • y: True target values
Returns: R² score (coefficient of determination)

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Attributes

After fitting, the following attributes are available:
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coef_
Vector
Estimated coefficients for the linear regression problem
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intercept_
number
Independent term in the linear model
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fitBackend_
'zig'
The backend used for fitting (always 'zig' for LinearRegression)
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fitBackendLibrary_
string | null
Path to the native library used for fitting

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Complete Example

import { LinearRegression } from "bun-scikit";

// Training data: y = 2x + 1
const X = [[1], [2], [3], [4], [5]];
const y = [3, 5, 7, 9, 11];

// Create and fit the model
const model = new LinearRegression({ solver: "normal" });
model.fit(X, y);

// Check the learned parameters
console.log("Intercept:", model.intercept_); // ~1
console.log("Coefficient:", model.coef_[0]); // ~2

// Make predictions
const prediction = model.predict([[6]])[0];
console.log("Prediction for x=6:", prediction); // ~13

// Evaluate the model
const r2 = model.score(X, y);
console.log("R² score:", r2); // ~1.0

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Notes

  • LinearRegression requires native Zig kernels. Build them with bun run native:build if not using prebuilt binaries.
  • The normal equation solver has O(n³) time complexity for feature count n, so may be slow for very high-dimensional data.
  • For regularized linear regression, see Ridge, Lasso, or ElasticNet.

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