8351
1935
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier)
sklearn.pipeline.Pipeline
1
hyperimp==0.0.1,openml==0.6.0,sklearn==0.19.1
Automatically created scikit-learn flow.
2018-04-14T05:26:12
English
sklearn==0.19.1
numpy>=1.6.1
scipy>=0.9
memory
null
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputation", "step_name": "imputation"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "hotencoding", "step_name": "hotencoding"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variencethreshold", "step_name": "variencethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "clf", "step_name": "clf"}}]
hotencoding
7644
3886
sklearn.preprocessing.data.OneHotEncoder
sklearn.preprocessing.data.OneHotEncoder
17
openml==0.6.0,sklearn==0.19.1
Automatically created scikit-learn flow.
2017-11-14T19:26:22
English
sklearn==0.19.1
numpy>=1.6.1
scipy>=0.9
categorical_features
[0, 1, 2, 3, 4, 5, 6, 7, 8]
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
handle_unknown
"ignore"
n_values
"auto"
sparse
true
openml-python
python
scikit-learn
sklearn
sklearn_0.19.1
variencethreshold
7645
3886
sklearn.feature_selection.variance_threshold.VarianceThreshold
sklearn.feature_selection.variance_threshold.VarianceThreshold
11
openml==0.6.0,sklearn==0.19.1
Automatically created scikit-learn flow.
2017-11-14T19:26:22
English
sklearn==0.19.1
numpy>=1.6.1
scipy>=0.9
threshold
0.0
openml-python
python
scikit-learn
sklearn
sklearn_0.19.1
clf
7684
4404
sklearn.ensemble.forest.RandomForestClassifier
sklearn.ensemble.forest.RandomForestClassifier
32
openml==0.6.0,sklearn==0.19.1
Automatically created scikit-learn flow.
2017-12-12T01:04:32
English
sklearn==0.19.1
numpy>=1.6.1
scipy>=0.9
bootstrap
true
class_weight
null
criterion
"gini"
max_depth
null
max_features
"auto"
max_leaf_nodes
null
min_impurity_decrease
0.0
min_impurity_split
null
min_samples_leaf
1
min_samples_split
2
min_weight_fraction_leaf
0.0
n_estimators
10
n_jobs
1
oob_score
false
random_state
null
verbose
0
warm_start
false
openml-python
python
scikit-learn
sklearn
sklearn_0.19.1
Verified_Supervised_Classification
imputation
8352
1935
hyperimp.utils.preprocessing.ConditionalImputer2
hyperimp.utils.preprocessing.ConditionalImputer2
1
hyperimp==0.0.1,openml==0.6.0
Automatically created scikit-learn flow.
2018-04-14T05:26:12
English
sklearn==0.19.1
numpy>=1.6.1
scipy>=0.9
axis
0
categorical_features
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
copy
true
fill_empty
0
missing_values
"NaN"
strategy
"mean"
strategy_nominal
"most_frequent"
verbose
0
openml-python
python
scikit-learn
sklearn
sklearn_0.19.1
openml-python
python
scikit-learn
sklearn
sklearn_0.19.1
study_98