2146 3229 TEST205a98e56csklearn.linear_model._base.LinearRegression sklearn.LinearRegression sklearn.linear_model._base.LinearRegression 1 openml==0.16.0,sklearn==1.5.2 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. 2026-02-11T18:26:57 English sklearn==1.5.2 numpy>=1.19.5 scipy>=1.6.0 joblib>=1.2.0 threadpoolctl>=3.1.0 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 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 <n_jobs>` for more details 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_1.5.2