DataPredict

API Reference - Models - NormalLinearRegression

NormalLinearRegression is a supervised machine learning model that predicts continuous values (e.g. 1.2, -32, 90, -1.2 and etc. ). It uses matrix 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.

NormalLinearRegression.new(): ModelObject

Returns:

• ModelObject: The generated model object.

Functions

train()

Train the model.

NormalLinearRegression:train(featureMatrix: Matrix, labelVector: Matrix)

Parameters:

• featureMatrix: Matrix containing all data.

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

predict()

Predict the value for a given data.

NormalLinearRegression:predict(featureMatrix: Matrix): number

Parameters:

• featureMatrix: Matrix containing data.

Returns:

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

Inherited From

• BaseModel