26473
1159
TESTd58feacf08sklearn.linear_model._base.LinearRegression
sklearn.LinearRegression
sklearn.linear_model._base.LinearRegression
1
openml==0.14.1,sklearn==0.24.0
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.
2024-01-15T11:06:03
English
sklearn==0.24.0
numpy>=1.13.3
scipy>=0.19.1
joblib>=0.11
threadpoolctl>=2.0.0
copy_X
bool
true
If True, X will be copied; else, it may be overwritten
fit_intercept
bool
true
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)
n_jobs
int
null
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 :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
normalize
bool
false
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
:class:`~sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``
positive
bool
false
When set to ``True``, forces the coefficients to be positive. This
option is only supported for dense arrays
.. versionadded:: 0.24
openml-python
python
scikit-learn
sklearn
sklearn_0.24.0