8317
1935
sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)
sklearn.pipeline.Pipeline
1
hyperimp==0.0.1,openml==0.6.0,sklearn==0.19.1
Automatically created scikit-learn flow.
2018-03-28T22:51:18
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": "scaling", "step_name": "scaling"}}, {"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
scaling
7646
3886
sklearn.preprocessing.data.StandardScaler
sklearn.preprocessing.data.StandardScaler
5
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
copy
true
with_mean
false
with_std
true
openml-python
python
scikit-learn
sklearn
sklearn_0.19.1
clf
7650
3886
sklearn.svm.classes.SVC
sklearn.svm.classes.SVC
16
openml==0.6.0,sklearn==0.19.1
Automatically created scikit-learn flow.
2017-11-14T19:26:47
English
sklearn==0.19.1
numpy>=1.6.1
scipy>=0.9
C
1.0
cache_size
1000
class_weight
null
coef0
0.0
decision_function_shape
"ovr"
degree
3
gamma
"auto"
kernel
"rbf"
max_iter
10000
probability
true
random_state
null
shrinking
true
tol
0.001
verbose
false
openml-python
python
scikit-learn
sklearn
sklearn_0.19.1
imputation
8316
1935
hyperimp.utils.preprocessing.ConditionalImputer
hyperimp.utils.preprocessing.ConditionalImputer
1
hyperimp==0.0.1,openml==0.6.0
Automatically created scikit-learn flow.
2018-03-26T22:41:03
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