DataPredict

API Reference - Models - LinearRegression

LinearRegression is a supervised machine learning model that predicts continuous values (e.g. 1.2, -32, 90, -1.2 and etc. ). It uses iterative calculations to find the best model parameters.

Stored Model Parameters

Contains a matrix.

• ModelParameters[I][J]: Value of matrix at row I and column J. The rows are the features.

Constructors

new()

Create new model object. If any of the arguments are nil, default argument values for that argument will be used.

LinearRegression.new(maximumNumberOfIterations: integer, learningRate: number, lossFunction: string): ModelObject

Parameters:

• maximumNumberOfIterations: How many times should the model needed to be trained.

• learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

• lossFunction: The function to calculate the cost of each training. Available options are “L1” and “L2”.

Returns:

• ModelObject: The generated model object.

Functions

setParameters()

Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.

LinearRegression:setParameters(maximumNumberOfIterations: integer, learningRate: number, lossFunction: string)

Parameters:

• maximumNumberOfIterations: How many times should the model needed to be trained.

• learningRate: The speed at which the model learns. Recommended that the value is set between (0 to 1).

• lossFunction: The function to calculate the cost of each training. Available options are “L1” and “L2”.

setOptimizer()

Set optimizer for the model by inputting the optimizer object.

LinearRegression:setOptimizer(Optimizer: OptimizerObject)

Parameters:

• Optimizer: The optimizer object to be used.

setRegularization()

Set a regularization for the model by inputting the optimizer object.

LinearRegression:setRegularization(Regularization: RegularizationObject)

Parameters:

• Regularization: The regularization to be used.

train()

Train the model.

LinearRegression:train(featureMatrix: Matrix, labelVector: Matrix): number[]

Parameters:

• featureMatrix: Matrix containing all data.

• labelVector: A (n x 1) matrix containing values related to featureMatrix.

Returns:

• costArray: An array containing cost values.

predict()

Predict the values for given data.

LinearRegression:predict(featureMatrix: Matrix): number

Parameters:

• featureMatrix: Matrix containing data.

Returns:

• predictedValue: A value that is predicted by the model.

Inherited From

• GradientMethodBaseModel