Run 10462000

Task 3891 (Supervised Classification) gina_agnostic Uploaded 20-05-2020 by Marc Zöller
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sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPC A,step_1=sklearn.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	0.8452552195064909
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"entropy"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	39
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.4990100849502301
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	2840
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.06523799541907112
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.042007886434321164
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.4474032919699697
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.4519744554712791
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"best"
sklearn.decomposition._kernel_pca.KernelPCA(1)_alpha	1.0
sklearn.decomposition._kernel_pca.KernelPCA(1)_coef0	1
sklearn.decomposition._kernel_pca.KernelPCA(1)_copy_X	false
sklearn.decomposition._kernel_pca.KernelPCA(1)_degree	3
sklearn.decomposition._kernel_pca.KernelPCA(1)_eigen_solver	"dense"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transform	false
sklearn.decomposition._kernel_pca.KernelPCA(1)_gamma	null
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel	"cosine"
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel_params	null
sklearn.decomposition._kernel_pca.KernelPCA(1)_max_iter	300
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components	355
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_jobs	1
sklearn.decomposition._kernel_pca.KernelPCA(1)_random_state	42
sklearn.decomposition._kernel_pca.KernelPCA(1)_remove_zero_eig	true
sklearn.decomposition._kernel_pca.KernelPCA(1)_tol	0
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_memory	null
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose	false