Run 10458261

Task 9981 (Supervised Classification) cnae-9 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=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	0.9178552454607742
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	791
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.7829638993572531
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	866
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.11275746308920637
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.4899084239576812
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.012888006989397025
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.06058242372397843
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	-0.10623080609514624
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	"sigmoid"
sklearn.decomposition._kernel_pca.KernelPCA(1)_kernel_params	null
sklearn.decomposition._kernel_pca.KernelPCA(1)_max_iter	57
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components	844
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