LinearRegression¶

class
getml.predictors.
LinearRegression
(learning_rate=0.9, reg_lambda=1e10)¶ Bases:
getml.predictors._Predictor
Simple predictor for regression problems.
Learns a simple linear relationship using ordinary least squares (OLS) regression:
The weights are optimized by minimizing the squared loss of the predictions w.r.t. the targets .
Linear regressions can be trained arithmetically or numerically. Training arithmetically is more accurate, but suffers worse scalability.
If you decide to pass categorical features to the
LinearRegression
, it will be trained numerically. Otherwise, it will be trained arithmetically. Parameters
learning_rate (float, optional) – The learning rate used for training numerically (only relevant when categorical features are included). Range: (0, ]
reg_lambda (float, optional) – L2 regularization parameter. Range: [0, ]
 Raises
TypeError – If any of the input arguments does not match its expected type.
Methods Summary
validate
([params])Checks both the types and the values of all instance variables and raises an exception if something is off.
Methods Documentation

validate
(params=None)¶ Checks both the types and the values of all instance variables and raises an exception if something is off.
 Parameters
params (dict, optional) – A dictionary containing the parameters to validate. If not is passed, the own parameters will be validated.
Examples
l = getml.predictors.LinearRegression() l.learning_rate = 8.1 l.validate()
 Raises
KeyError – If an unsupported instance variable is encountered.
TypeError – If any instance variable is of wrong type.
ValueError – If any instance variable does not match its possible choices (string) or is out of the expected bounds (numerical).
Note
This method is called at end of the __init__ constructor and every time before the predictor  or a class holding it as an instance variable  is send to the getML engine.