LogisticRegression is a supervised machine learning model that predicts values of 0 and 1 only.
Contains a matrix.
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
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”.
Set model’s parameters. When any of the arguments are nil, previous argument values for that argument will be used.
LogisticRegression:setParameters(maximumNumberOfIterations: integer, learningRate: number, sigmoidFunction: string)
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”.
Set optimizer for the model by inputting the optimizer object.
LogisticRegression:setOptimizer(Optimizer: OptimizerObject)
Set a regularization for the model by inputting the optimizer object.
LogisticRegression:setRegularization(Regularization: RegularizationObject)
Train the model.
LogisticRegression:train(featureMatrix: Matrix, labelVector: Matrix): number[]
featureMatrix: Matrix containing all data.
labelVector: A (n x 1) matrix containing values related to featureMatrix.
Predict the values for given data.
LogisticRegression:predict(featureMatrix: Matrix, returnOriginalOutput: boolean): Matrix, Matrix -OR- Matrix
featureMatrix: Matrix containing all data.
returnOriginalOutput: Set whether or not to return predicted matrix instead of value with highest probability.
predictedVector: A vector that is predicted by the model.
probabilityVector: A vector that contains the probability of predicted values in predictedVector.
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