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.
Note
This class will be trained using the
fit()
method and used for prediction using thepredict()
method of eitherMultirelModel
orRelboostModel
.Methods Summary
validate
()Checks both the types and the values of all instance variables and raises an exception if something is off.
Methods Documentation

validate
()¶ Checks both the types and the values of all instance variables and raises an exception if something is off.
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.