Visibility: public Uploaded 25-11-2022 by Continuous Integration sklearn==0.24.0 numpy>=1.13.3 scipy>=0.19.1 joblib>=0.11 threadpoolctl>=2.0.0 1 runs
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  • openml-python python scikit-learn sklearn sklearn_0.24.0
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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.


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 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 ` for more detailsdefault: null
normalizeThis 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``default: false
positiveWhen set to ``True``, forces the coefficients to be positive. This option is only supported for dense arrays .. versionadded:: 0.24default: false


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