Run 10455833

Task 9914 (Supervised Classification) wilt Uploaded 19-05-2020 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	0.5815764521928262
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	0.047791144173372546
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1722
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.05038847686323531
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.31181927511118646
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.4808531384825606
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.3453034069124774
sklearn.tree._classes.DecisionTreeClassifier(3)_presort	"deprecated"
sklearn.tree._classes.DecisionTreeClassifier(3)_random_state	42
sklearn.tree._classes.DecisionTreeClassifier(3)_splitter	"random"
sklearn.decomposition._kernel_pca.KernelPCA(1)_alpha	1.0
sklearn.decomposition._kernel_pca.KernelPCA(1)_coef0	0.12011446609699994
sklearn.decomposition._kernel_pca.KernelPCA(1)_copy_X	false
sklearn.decomposition._kernel_pca.KernelPCA(1)_degree	6
sklearn.decomposition._kernel_pca.KernelPCA(1)_eigen_solver	"arpack"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transform	false
sklearn.decomposition._kernel_pca.KernelPCA(1)_gamma	1.7975872352042979e-09
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel	"poly"
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel_params	null
sklearn.decomposition._kernel_pca.KernelPCA(1)_max_iter	726
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components	3
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	0.2717288731832881
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