Linear Regression

../../../../_images/linear_regression.svg

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]

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. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems.
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_
Inputs:
Outputs:
model : model

Model