Linear Regression¶
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 (e.g. data is expected to be already 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 usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=False
.n_jobs
The number of jobs to use for the computation. This will only provide speedup for n_targets > 1 and sufficient large problems.
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.
residues_
Input ports:
- Output ports:
- model : model
- Model
- fit_intercept (fit_intercept)
- whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g. data is expected to be already centered).
- normalize (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 usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=False
. - n_jobs (n_jobs)
- The number of jobs to use for the computation. This will only provide
speedup for n_targets > 1 and sufficient large problems.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See n_jobs for more details.
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.