API Reference - Models - LogisticRegression

LogisticRegression is a supervised machine learning model that predicts values of 0 and 1 only.

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.

LogisticRegression.new(maximumNumberOfIterations: integer, learningRate: number, sigmoidFunction: 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.

  • sigmoidFunction: The function to calculate the cost and cost derivaties of each training. Available options are:

    • Sigmoid (Default)

    • Tanh

    • HardSigmoid

    • SoftSign

    • Swish

    • ArcTangent

    • Swish

    • BipolarSigmoid

Returns:

  • ModelObject: The generated model object.

Functions

setOptimizer()

Set optimizer for the model by inputting the optimizer object.

LogisticRegression:setOptimizer(Optimizer: OptimizerObject)

Parameters:

  • Optimizer: The optimizer object to be used.

setRegularizer()

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

LogisticRegression:setRegularizer(Regularizer: RegularizerObject)

Parameters:

  • Regularizer: The regularizer to be used.

train()

Train the model.

LogisticRegression: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.

LogisticRegression:predict(featureMatrix: Matrix, returnOriginalOutput: boolean): Matrix, Matrix -OR- Matrix

Parameters:

  • featureMatrix: Matrix containing all data.

  • returnOriginalOutput: Set whether or not to return predicted matrix instead of value with highest probability.

Returns:

  • predictedVector: A vector that is predicted by the model.

  • probabilityVector: A vector that contains the probability of predicted values in predictedVector.

-OR-

  • predictedMatrix: A matrix containing all predicted values from all classes.

Inherited From