php-ml/docs/machine-learning/regression/least-squares.md

LeastSquares Linear Regression

Linear model that uses least squares method to approximate solution.

Train

To train a model, simply provide train samples and targets values (as array). Example:

$samples = [[60], [61], [62], [63], [65]];
$targets = [3.1, 3.6, 3.8, 4, 4.1];

$regression = new LeastSquares();
$regression->train($samples, $targets);

You can train the model using multiple data sets, predictions will be based on all the training data.

Predict

To predict sample target value, use the predict method with sample to check (as array). Example:

$regression->predict([64]);
// return 4.06

Multiple Linear Regression

The term multiple attached to linear regression means that there are two or more sample parameters used to predict target. For example you can use: mileage and production year to predict the price of a car.

$samples = [[73676, 1996], [77006, 1998], [10565, 2000], [146088, 1995], [15000, 2001], [65940, 2000], [9300, 2000], [93739, 1996], [153260, 1994], [17764, 2002], [57000, 1998], [15000, 2000]];
$targets = [2000, 2750, 15500, 960, 4400, 8800, 7100, 2550, 1025, 5900, 4600, 4400];

$regression = new LeastSquares();
$regression->train($samples, $targets);
$regression->predict([60000, 1996])
// return 4094.82

Intercept and Coefficients

After you train your model, you can get the intercept and coefficients array.

$regression->getIntercept();
// return -7.9635135135131

$regression->getCoefficients();
// return [array(1) {[0]=>float(0.18783783783783)}]