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(lambda: number): ModelObject
Parameters:
- lambda: Sets the value for L2 regularization.
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): Matrix
Parameters:
- featureMatrix: Matrix containing data.
Returns:
- predictedVector: A vector containing values that are predicted by the model.