Offline Training Algorithms#


Get all supported offline training methods.

register_offline_method(name, method)

Register a new offline learning method.


Base class for offline training algorithm.

LinearRegression([name, max_iter, ...])

Training algorithm of least-square regression.

RidgeRegression([alpha, beta, name, ...])

Training algorithm of ridge regression.

LassoRegression([alpha, degree, add_bias, ...])

Lasso regression method for offline training.

LogisticRegression([learning_rate, ...])

Logistic regression method for offline training.

PolynomialRegression([degree, name, ...])

PolynomialRidgeRegression([alpha, degree, ...])

ElasticNetRegression([alpha, degree, ...])

Parameters: degree: int The degree of the polynomial that the independent variable X will be transformed to. reg_factor: float The factor that will determine the amount of regularization and feature shrinkage. l1_ration: float Weighs the contribution of l1 and l2 regularization. n_iterations: float The number of training iterations the algorithm will tune the weights for. learning_rate: float The step length that will be used when updating the weights.