Linear Regression¶
Ordinary linear regression
Documentation¶
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_
Definition¶
Output ports¶
- model model
Model
Configuration¶
- Fit intercept (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).
- Number of jobs (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.
Implementation¶
- class node_regression.LinearRegression[source]