| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEn
coder,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(
DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.pre
processing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.Dec
isionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassif
ier))(1) | Automatically created scikit-learn flow. |
| sklearn.tree.tree.DecisionTreeClassifier(6)_class_weight | None |
| sklearn.tree.tree.DecisionTreeClassifier(6)_criterion | gini |
| sklearn.tree.tree.DecisionTreeClassifier(6)_max_depth | None |
| sklearn.tree.tree.DecisionTreeClassifier(6)_max_features | None |
| sklearn.tree.tree.DecisionTreeClassifier(6)_max_leaf_nodes | None |
| sklearn.tree.tree.DecisionTreeClassifier(6)_min_impurity_split | 1e-07 |
| sklearn.tree.tree.DecisionTreeClassifier(6)_min_samples_leaf | 1 |
| sklearn.tree.tree.DecisionTreeClassifier(6)_min_samples_split | 2 |
| sklearn.tree.tree.DecisionTreeClassifier(6)_min_weight_fraction_leaf | 0.0 |
| sklearn.tree.tree.DecisionTreeClassifier(6)_presort | False |
| sklearn.tree.tree.DecisionTreeClassifier(6)_random_state | None |
| sklearn.tree.tree.DecisionTreeClassifier(6)_splitter | best |
| sklearn.preprocessing.data.OneHotEncoder(3)_categorical_features | [False, False, False, False, False, False, False, False] |
| sklearn.preprocessing.data.OneHotEncoder(3)_dtype | |
| sklearn.preprocessing.data.OneHotEncoder(3)_handle_unknown | error |
| sklearn.preprocessing.data.OneHotEncoder(3)_n_values | auto |
| sklearn.preprocessing.data.OneHotEncoder(3)_sparse | False |
| sklearn.tree.tree.ExtraTreeClassifier(1)_class_weight | None |
| sklearn.tree.tree.ExtraTreeClassifier(1)_criterion | gini |
| sklearn.tree.tree.ExtraTreeClassifier(1)_max_depth | None |
| sklearn.tree.tree.ExtraTreeClassifier(1)_max_features | auto |
| sklearn.tree.tree.ExtraTreeClassifier(1)_max_leaf_nodes | None |
| sklearn.tree.tree.ExtraTreeClassifier(1)_min_impurity_split | 1e-07 |
| sklearn.tree.tree.ExtraTreeClassifier(1)_min_samples_leaf | 1 |
| sklearn.tree.tree.ExtraTreeClassifier(1)_min_samples_split | 2 |
| sklearn.tree.tree.ExtraTreeClassifier(1)_min_weight_fraction_leaf | 0.0 |
| sklearn.tree.tree.ExtraTreeClassifier(1)_random_state | None |
| sklearn.tree.tree.ExtraTreeClassifier(1)_splitter | random |
| sklearn.preprocessing.data.StandardScaler(1)_copy | True |
| sklearn.preprocessing.data.StandardScaler(1)_with_mean | True |
| sklearn.preprocessing.data.StandardScaler(1)_with_std | True |
| sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier))(1)_steps | [('onehotencoder', OneHotEncoder(categorical_features=[False, False, False, False, False, False, False, False],
dtype=, handle_unknown='error',
n_values='auto', sparse=False)), ('votingclassifier', VotingClassifier(estimators=[('DecisionTreeClassifier', Pipeline(steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('decisiontreeclassifier', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
...min_samples_split=2, min_weight_fraction_leaf=0.0,
random_state=None, splitter='random'))],
n_jobs=1, voting='soft', weights=None))] |
| sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(1)_estimators | [('DecisionTreeClassifier', Pipeline(steps=[('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('decisiontreeclassifier', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=None, splitter='best'))])), ('ExtraTreeClassifier', ExtraTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
random_state=None, splitter='random'))] |
| sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(1)_n_jobs | 1 |
| sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(1)_voting | soft |
| sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)(1)_weights | None |
| sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_steps | [('standardscaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('decisiontreeclassifier', DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
presort=False, random_state=None, splitter='best'))] |