Change Details

Updated scikit-learn to 0.21.2 **Changes**: - Fixed a bug in `cross_decomposition.CCA` improving numerical stability when `Y` is close to zero - Fixed a bug in `metrics.pairwise.euclidean_distances` where a part of the distance matrix was left un-instanciated for suffiently large float32 datasets (regression introduced in 0.21). - Fixed a bug in `preprocessing.OneHotEncoder` where the new drop parameter was not reflected in get_feature_names. - Fixed an issue with `plot_tree` where it display entropy calculations even for gini criterion in DecisionTreeClassifiers - Fixed a bug where `min_max_axis` would fail on 32-bit systems for certain large inputs. This affects `preprocessing.MaxAbsScaler`, `preprocessing.normalize` and `preprocessing.LabelBinarizer`.

Updated scikit-learn to 0.21. **Changes**: - Fixed a bug in `cross_decomposition.CCA` improving numerical stability when `Y` is close to zero - Fixed a bug in `metrics.pairwise.euclidean_distances` where a part of the distance matrix was left un-instanciated for suffiently large float32 datasets (regression introduced in 0.21). - Fixed a bug in `preprocessing.OneHotEncoder` where the new drop parameter was not reflected in get_feature_names. - Fixed an issue with `plot_tree` where it display entropy calculations even for gini criterion in DecisionTreeClassifiers - Fixed a bug where `min_max_axis` would fail on 32-bit systems for certain large inputs. This affects `preprocessing.MaxAbsScaler`, `preprocessing.normalize` and `preprocessing.LabelBinarizer`.

Updated scikit-learn to 0.21.2 **Changes**: - Fixed a bug in `cross_decomposition.CCA` improving numerical stability when `Y` is close to zero - Fixed a bug in `metrics.pairwise.euclidean_distances` where a part of the distance matrix was left un-instanciated for suffiently large float32 datasets (regression introduced in 0.21). - Fixed a bug in `preprocessing.OneHotEncoder` where the new drop parameter was not reflected in get_feature_names. - Fixed an issue with `plot_tree` where it display entropy calculations even for gini criterion in DecisionTreeClassifiers - Fixed a bug where `min_max_axis` would fail on 32-bit systems for certain large inputs. This affects `preprocessing.MaxAbsScaler`, `preprocessing.normalize` and `preprocessing.LabelBinarizer`.