| Issue | #Downvotes for this reason | By |
|---|
| sklearn.svm._classes.SVR(1) | Epsilon-Support Vector Regression. The free parameters in the model are C and epsilon. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to datasets with more than a couple of 10000 samples. For large datasets consider using :class:`sklearn.svm.LinearSVR` or :class:`sklearn.linear_model.SGDRegressor` instead, possibly after a :class:`sklearn.kernel_approximation.Nystroem` transformer. |
| sklearn.svm._classes.SVR(1)_C | 1.0 |
| sklearn.svm._classes.SVR(1)_cache_size | 200 |
| sklearn.svm._classes.SVR(1)_coef0 | 0.0 |
| sklearn.svm._classes.SVR(1)_degree | 3 |
| sklearn.svm._classes.SVR(1)_epsilon | 0.1 |
| sklearn.svm._classes.SVR(1)_gamma | "scale" |
| sklearn.svm._classes.SVR(1)_kernel | "rbf" |
| sklearn.svm._classes.SVR(1)_max_iter | -1 |
| sklearn.svm._classes.SVR(1)_shrinking | true |
| sklearn.svm._classes.SVR(1)_tol | 0.001 |
| sklearn.svm._classes.SVR(1)_verbose | 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)
|