Run 10464578

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads

Issue	#Downvotes for this reason	By

Flow

sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeat ures,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)	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.ensemble._forest.RandomForestClassifier(2)_bootstrap	true
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha	0.29801656791435893
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	38
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	23
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	10533
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.3194094987437691
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.7417365226584083
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.4572186997480571
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.007542808880394591
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.13572294444163235
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	983
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs	1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_score	false
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state	42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose	0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_start	false
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps	[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false
sklearn.preprocessing._data.PolynomialFeatures(1)_degree	2
sklearn.preprocessing._data.PolynomialFeatures(1)_include_bias	false
sklearn.preprocessing._data.PolynomialFeatures(1)_interaction_only	true
sklearn.preprocessing._data.PolynomialFeatures(1)_order	"C"