Issue | #Downvotes for this reason | By |
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sklearn.linear_model._base.LinearRegression(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. |
sklearn.linear_model._base.LinearRegression(2)_copy_X | true |
sklearn.linear_model._base.LinearRegression(2)_fit_intercept | true |
sklearn.linear_model._base.LinearRegression(2)_n_jobs | null |
sklearn.linear_model._base.LinearRegression(2)_normalize | false |
1.6358 ± 0.1126 Cross-validation details (10-fold Crossvalidation)
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1000 Cross-validation details (10-fold Crossvalidation) |
1.9819 ± 0.1592 Cross-validation details (10-fold Crossvalidation)
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