Run 10454154

Task 3735 (Supervised Classification) pollen Uploaded 18-05-2020 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.QuantileTransf ormer,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	0.27659376984132344
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weight	null
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion	"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth	2
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features	1
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes	2924
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples	0.9183955661373606
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease	0.47084826319084505
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_split	null
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf	0.1435117977491629
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split	0.30755285635122503
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf	0.46460208889392096
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators	1232
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.QuantileTransformer,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,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.QuantileTransformer,step_1=sklearn.ensemble._forest.RandomForestClassifier)(1)_verbose	false
sklearn.preprocessing._data.QuantileTransformer(1)_copy	false
sklearn.preprocessing._data.QuantileTransformer(1)_ignore_implicit_zeros	true
sklearn.preprocessing._data.QuantileTransformer(1)_n_quantiles	272
sklearn.preprocessing._data.QuantileTransformer(1)_output_distribution	"normal"
sklearn.preprocessing._data.QuantileTransformer(1)_random_state	42
sklearn.preprocessing._data.QuantileTransformer(1)_subsample	68502982