API Reference - Models - RecursiveLeastSquaresRegression
IterativeReweightedLeastSquaresRegression 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.
RecursiveLeastSquaresRegression.new(forgetFactor: number, useLogProbabilities: boolean): ModelObject
Parameters:
-
forgetFactor: How much should the past data influence the next weight updates. [Default: 1]
-
useLogProbabilities: Set whether or not the predict() function would use log probabilities instead of raw probabilities.
Returns:
- ModelObject: The generated model object.
Functions
train()
Train the model.
RecursiveLeastSquaresRegression: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.
RecursiveLeastSquaresRegression:predict(featureMatrix: Matrix, thresholdMatrix: Matrix): Matrix -OR- Matrix, Matrix
Parameters:
-
featureMatrix: Matrix containing data.
-
thresholdMatrix: A matrix of threshold values for computing predicted probabilities. If provided, the model returns both predicted values and the probability that the prediction exceeds the threshold(s).
Returns:
- predictedVector: A vector containing values that are predicted by the model.
– OR –
-
predictedVector: A vector containing values that are predicted by the model.
-
predictedProbabilityMatrix: A matrix contining the probability of the values with the given threshold.