| Issue | #Downvotes for this reason | By |
|---|
| 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 |
3.7333 ± 0.3417 Cross-validation details (10 times 10-fold Crossvalidation)
|
3600 Cross-validation details (10 times 10-fold Crossvalidation) |
4.3205 ± 0.3127 Cross-validation details (10 times 10-fold Crossvalidation)
|