Flow
TEST6fa1e3d495sklearn.linear_model._base.LinearRegression

TEST6fa1e3d495sklearn.linear_model._base.LinearRegression

Visibility: public Uploaded 17-10-2024 by Continuous Integration sklearn==1.3.2 numpy>=1.17.3 scipy>=1.5.0 joblib>=1.1.1 threadpoolctl>=2.0.0 1 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_1.3.2
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.

Parameters

copy_XIf True, X will be copied; else, it may be overwrittendefault: true
fit_interceptWhether 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)default: true
n_jobsThe 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 a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more detailsdefault: null
positiveWhen set to ``True``, forces the coefficients to be positive. This option is only supported for dense arrays .. versionadded:: 0.24default: false

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table