Run 10418933

Task 3 (Supervised Classification) kr-vs-kp Uploaded 03-12-2019 by Heinrich Peters
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm .classes.SVC)(3)	Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.impute._base.SimpleImputer(4)_add_indicator	false
sklearn.impute._base.SimpleImputer(4)_copy	true
sklearn.impute._base.SimpleImputer(4)_fill_value	null
sklearn.impute._base.SimpleImputer(4)_missing_values	NaN
sklearn.impute._base.SimpleImputer(4)_strategy	"most_frequent"
sklearn.impute._base.SimpleImputer(4)_verbose	0
sklearn.svm.classes.SVC(37)_C	170.5572031055097
sklearn.svm.classes.SVC(37)_cache_size	200
sklearn.svm.classes.SVC(37)_class_weight	null
sklearn.svm.classes.SVC(37)_coef0	0.7430320055311159
sklearn.svm.classes.SVC(37)_decision_function_shape	"ovr"
sklearn.svm.classes.SVC(37)_degree	2
sklearn.svm.classes.SVC(37)_gamma	0.049727598083142026
sklearn.svm.classes.SVC(37)_kernel	"poly"
sklearn.svm.classes.SVC(37)_max_iter	-1
sklearn.svm.classes.SVC(37)_probability	true
sklearn.svm.classes.SVC(37)_random_state	1
sklearn.svm.classes.SVC(37)_shrinking	true
sklearn.svm.classes.SVC(37)_tol	0.001
sklearn.svm.classes.SVC(37)_verbose	false
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(3)_memory	null
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(3)_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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svc", "step_name": "svc"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC)(3)_verbose	false
sklearn.preprocessing._encoders.OneHotEncoder(18)_categorical_features	null
sklearn.preprocessing._encoders.OneHotEncoder(18)_categories	null
sklearn.preprocessing._encoders.OneHotEncoder(18)_drop	null
sklearn.preprocessing._encoders.OneHotEncoder(18)_dtype	{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(18)_handle_unknown	"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(18)_n_values	null
sklearn.preprocessing._encoders.OneHotEncoder(18)_sparse	true