8817
6892
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,svc=sklearn.svm.classes.SVC)
sklearn.pipeline.Pipeline
1
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-20T00:56:11
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
memory
null
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
svc
8785
1
sklearn.svm.classes.SVC
sklearn.svm.classes.SVC
22
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-09T02:18:13
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
C
143.11841724167172
cache_size
200
class_weight
null
coef0
0.22921525238301776
decision_function_shape
"ovr"
degree
2
gamma
0.0070192306738709525
kernel
"poly"
max_iter
-1
probability
false
random_state
null
shrinking
false
tol
1.1571247532842242e-05
verbose
false
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
columntransformer
8812
1
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))
sklearn.compose._column_transformer.ColumnTransformer
1
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-18T22:00:43
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
n_jobs
null
remainder
"passthrough"
sparse_threshold
0.3
transformer_weights
null
transformers
[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}]
nominal
8780
1
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)
sklearn.pipeline.Pipeline
1
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-07T23:00:45
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
memory
null
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}]
simpleimputer
8781
1
sklearn.impute.SimpleImputer
sklearn.impute.SimpleImputer
1
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-07T23:00:45
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
copy
true
fill_value
-1
missing_values
NaN
strategy
"constant"
verbose
0
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
onehotencoder
8782
1
sklearn.preprocessing._encoders.OneHotEncoder
sklearn.preprocessing._encoders.OneHotEncoder
3
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-07T23:00:45
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
categorical_features
null
categories
null
dtype
{"oml-python:serialized_object": "type", "value": "np.float64"}
handle_unknown
"ignore"
n_values
null
sparse
true
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
numeric
8813
1
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)
sklearn.pipeline.Pipeline
1
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-18T22:00:43
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
memory
null
steps
[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}]
imputer
8778
1
sklearn.preprocessing.imputation.Imputer
sklearn.preprocessing.imputation.Imputer
29
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-07T23:00:45
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
axis
0
copy
true
missing_values
"NaN"
strategy
"mean"
verbose
0
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
standardscaler
8779
1
sklearn.preprocessing.data.StandardScaler
sklearn.preprocessing.data.StandardScaler
14
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-07T23:00:45
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
copy
true
with_mean
true
with_std
true
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
variancethreshold
8816
1
sklearn.feature_selection.variance_threshold.VarianceThreshold
sklearn.feature_selection.variance_threshold.VarianceThreshold
18
openml==0.8.0dev,sklearn==0.20.0
Automatically created scikit-learn flow.
2018-10-19T02:03:47
English
sklearn==0.20.0
numpy>=1.6.1
scipy>=0.9
threshold
0.0
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0
openml-python
python
scikit-learn
sklearn
sklearn_0.20.0