API Reference - Models - BinaryRegression

BinaryRegression is a supervised machine learning model that handles the generalized binary regression functions.

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.

BinaryRegression.new(maximumNumberOfIterations: integer, learningRate: number, binaryFunction: string, costFunction: 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.

  • binaryFunction: The activation function to be used by the model. Available options are:

Function Output Range Skewness Use Cases
Logistic (Default) (0, 1) Symmetric Player Choice (A/B), Engagement Prediction, Click-Through Rates
HardSigmoid (0, 1) Symmetric Same As Logistic, But Mobile / Real-Time Prediction
Probit (0, 1) Symmetric Skill-Based Success, Ability Checks, Normally Distributed Traits
BipolarSigmoid (-1, 1) Symmetric Win / Lose, Accept / Reject, Binary Outcomes With Magnitude
Tanh (-1, 1) Symmetric Like / Dislike, Positive / Negative Feedback, Preference Modeling
SoftSign (-1, 1) Symmetric Gradual Preference Changes, Soft Decisions
ArcTangent (-π/2, π/2) Symmetric Academic / Research Alternative To Tanh
ComplementaryLogLog (0, 1) Right-Skewed Rare Events Prediction: In-App Purchases, Time-To-Leave Prediction
LogLog (0, 1) Left-Skewed Common Events Prediction: Tutorial Completion, Early Wins, First Purchases

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

    • BinaryCrossEntropy (Default)

    • MeanSquaredError

    • MeanAbsoluteError

Returns:

  • ModelObject: The generated model object.

Functions

setOptimizer()

Set optimizer for the model by inputting the optimizer object.

BinaryRegression:setOptimizer(Optimizer: OptimizerObject)

Parameters:

  • Optimizer: The optimizer object to be used.

setRegularizer()

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

BinaryRegression:setRegularizer(Regularizer: RegularizerObject)

Parameters:

  • Regularizer: The regularizer to be used.

train()

Train the model.

BinaryRegression: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.

BinaryRegression: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