Run 10454556

Task 3735 (Supervised Classification) pollen 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	0.030154794632575532
sklearn.tree._classes.DecisionTreeClassifier(3)_class_weight	null
sklearn.tree._classes.DecisionTreeClassifier(3)_criterion	"gini"
sklearn.tree._classes.DecisionTreeClassifier(3)_max_depth	2
sklearn.tree._classes.DecisionTreeClassifier(3)_max_features	0.2430572823738298
sklearn.tree._classes.DecisionTreeClassifier(3)_max_leaf_nodes	1325
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_decrease	0.17869186244915658
sklearn.tree._classes.DecisionTreeClassifier(3)_min_impurity_split	null
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_leaf	0.14712161322335623
sklearn.tree._classes.DecisionTreeClassifier(3)_min_samples_split	0.0864804885270435
sklearn.tree._classes.DecisionTreeClassifier(3)_min_weight_fraction_leaf	0.4319962435273015
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	4
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	"arpack"
sklearn.decomposition._kernel_pca.KernelPCA(1)_fit_inverse_transform	true
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	524
sklearn.decomposition._kernel_pca.KernelPCA(1)_n_components	4
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	false
sklearn.decomposition._kernel_pca.KernelPCA(1)_tol	0.6476998368285369
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