API Reference - Models - PassiveAggressiveClassifier
PassiveAggressiveClasifier is a supervised machine learning model that predicts binary values (+1 and -1). 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.
PassiveAggressiveClassifier.new(maximumNumberOfIterations: integer, variant: string, cValue: number): ModelObject
Parameters:
-
maximumNumberOfIterations: How many times should the model needed to be trained. [Default: 500]
-
variant: Controls which PassiveAggressiveClasifier variant to use. Available options are:
-
0 (Default)
-
1
-
2
-
-
cValue: The aggressiveness parameter used in some Passive-Aggressive variants (1 and 2). Higher values allow larger updates per misclassified example, while lower values limit the update magnitude. [Default: 1]
Returns:
- ModelObject: The generated model object.
Functions
train()
Train the model.
PassiveAggressiveClassifier: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.
PassiveAggressiveClasifier:predict(featureMatrix: Matrix, returnOriginalOutput: boolean): Matrix -OR- Matrix
Parameters:
-
featureMatrix: Matrix containing data.
-
returnOriginalOutput: Set whether or not to return original output.
Returns:
- predictedLabelVector: A vector containing the classes that is predicted by the model.
-OR-
- predictedValueVector: A vector containing the values that is predicted by the model.