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 use
sklearn.preprocessing.StandardScaler before calling fit on
an estimator with normalize=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 a joblib.parallel_backend context.
-1 means using all processors. See n_jobs
for more details.
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| 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_
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| Inputs: | |
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| Outputs: |
- model : model
Model
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- Output ports:
-
- 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 use
sklearn.preprocessing.StandardScaler before calling fit on
an estimator with normalize=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 a joblib.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.
-
class
node_regression.LinearRegression[source]
