Run 10456258

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 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=automl.util.sklearn.StackingEstimator(esti mator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_1=skle arn.tree._classes.DecisionTreeClassifier)(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.tree._classes.DecisionTreeClassifier(3)_ccp_alpha	0.192267369540278
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	2
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.11304390271127057
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1171
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.12752136962673039
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.2318498769409779
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.09865772861566231
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3765527492715372
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components	245
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priors	null
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage	0.5848025244874058
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver	"lsqr"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariance	false
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol	3.2174152400242415e-07
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_1=sklearn.tree._classes.DecisionTreeClassifier)(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=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false