API Reference - Models - TweedieRegression
TweedieRegression is a supervised machine learning model that predicts continuous positive values. 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.
TweedieRegression.new(maximumNumberOfIterations: integer, learningRate: number, 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.
-
power: Controls the mean-variance relationship and selects the underlying distribution from the Tweedie family. [Default: 1.5]
-
power = 0: Normal distribution (for continuous data with constant variance)
-
power = 1: Poisson distribution (for count data)
-
power = 2: Gamma distribution (for continuous positive data with constant coefficient of variation)
-
power = 3: Inverse Gaussian distribution (for continuous positive data with heavy tails)
-
1 < power < 2: Compound Poisson-Gamma distribution (for data with exact zeros and positive continuous values)
-
Returns:
- ModelObject: The generated model object.
Functions
setOptimizer()
Set optimizer for the model by inputting the optimizer object.
TweedieRegression:setOptimizer(Optimizer: OptimizerObject)
Parameters:
- Optimizer: The optimizer object to be used.
setRegularizer()
Set a regularization for the model by inputting the optimizer object.
TweedieRegression:setRegularizer(Regularizer: RegularizerObject)
Parameters:
- Regularizer: The regularizer to be used.
setSolver()
Set a solver for the model by inputting the optimizer object.
TweedieRegression:setSolver(Solver: SolverObject)
Parameters:
- Solver: The solver to be used.
train()
Train the model.
TweedieRegression: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 value for a given data.
TweedieRegression:predict(featureMatrix: Matrix): Matrix
Parameters:
- featureMatrix: Matrix containing data.
Returns:
- predictedVector: A vector containing values that are predicted by the model.