Linear Regression¶
Ordinary linear regression
Documentation
Ordinary linear regression
Configuration:
fit_intercept
Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).
normalize
This parameter is ignored when
fit_intercept
is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please useStandardScaler
before callingfit
on an estimator withnormalize=False
.Deprecated since version 1.0: normalize was deprecated in version 1.0 and will be removed in 1.2.
n_jobs
The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstly n_targets > 1 and secondly X is sparse or if positive is set to True.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See n_jobs for more details.
Attributes:
coef_
Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.
intercept_
Independent term in the linear model. Set to 0.0 if fit_intercept = False.
residues_
Input ports:
- Output ports:
- modelmodel
Model
Definition
Input ports
Output ports
- model
model
Model
- class node_regression.LinearRegression[source]