{"flow":{"id":"18466","uploader":"12269","name":"sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._kernel_pca.KernelPCA,step_2=sklearn.tree._classes.DecisionTreeClassifier)","custom_name":"sklearn.Pipeline(MissingIndicator,KernelPCA,DecisionTreeClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Pipeline of transforms with a final estimator.\n\nSequentially apply a list of transforms and a final estimator.\nIntermediate steps of the pipeline must be 'transforms', that is, they\nmust implement fit and transform methods.\nThe final estimator only needs to implement fit.\nThe transformers in the pipeline can be cached using ``memory`` argument.\n\nThe purpose of the pipeline is to assemble several steps that can be\ncross-validated together while setting different parameters.\nFor this, it enables setting parameters of the various steps using their\nnames and the parameter name separated by a '__', as in the example below.\nA step's estimator may be replaced entirely by setting the parameter\nwith its name to another estimator, or a transformer removed by setting\nit to 'passthrough' or ``None``.","upload_date":"2020-05-21T08:02:54","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"memory","data_type":"None","default_value":"null","description":"Used to cache the fitted transformers of the pipeline. By default,\n no caching is performed. If a string is given, it is the path to\n the caching directory. Enabling caching triggers a clone of\n the transformers before fitting. Therefore, the transformer\n instance given to the pipeline cannot be inspected\n directly. Use the attribute ``named_steps`` or ``steps`` to\n inspect estimators within the pipeline. Caching the\n transformers is advantageous when fitting is time consuming"},{"name":"steps","data_type":"list","default_value":"[{\"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\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"step_2\", \"step_name\": \"step_2\"}}]","description":"List of (name, transform) tuples (implementing fit\/transform) that are\n chained, in the order in which they are chained, with the last object\n an estimator"},{"name":"verbose","data_type":"bool","default_value":"false","description":"If True, the time elapsed while fitting each step will be printed as it\n is completed."}],"component":[{"identifier":"step_2","flow":{"id":"17504","uploader":"11295","name":"sklearn.tree._classes.DecisionTreeClassifier","custom_name":"sklearn.DecisionTreeClassifier","class_name":"sklearn.tree._classes.DecisionTreeClassifier","version":"3","external_version":"openml==0.10.2,sklearn==0.22.1","description":"A decision tree classifier.","upload_date":"2020-02-08T19:46:35","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"ccp_alpha","data_type":"non","default_value":"0.0","description":"Complexity parameter used for Minimal Cost-Complexity Pruning. The\n subtree with the largest cost complexity that is smaller than\n ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n :ref:`minimal_cost_complexity_pruning` for details\n\n .. versionadded:: 0.22"},{"name":"class_weight","data_type":"dict","default_value":"null","description":"Weights associated with classes in the form ``{class_label: weight}``\n If None, all classes are supposed to have weight one. For\n multi-output problems, a list of dicts can be provided in the same\n order as the columns of y\n\n Note that for multioutput (including multilabel) weights should be\n defined for each class of every column in its own dict. For example,\n for four-class multilabel classification weights should be\n [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of\n [{1:1}, {2:5}, {3:1}, {4:1}]\n\n The \"balanced\" mode uses the values of y to automatically adjust\n weights inversely proportional to class frequencies in the input data\n as ``n_samples \/ (n_classes * np.bincount(y))``\n\n For multi-output, the weights of each column of y will be multiplied\n\n Note that these weights will be multiplied with sample_weight (passed\n through the fit method) if sample_weight is specified"},{"name":"criterion","data_type":[],"default_value":"\"gini\"","description":[]},{"name":"max_depth","data_type":"int","default_value":"null","description":"The maximum depth of the tree. If None, then nodes are expanded until\n all leaves are pure or until all leaves contain less than\n min_samples_split samples"},{"name":"max_features","data_type":"int","default_value":"null","description":"The number of features to consider when looking for the best split:\n\n - If int, then consider `max_features` features at each split\n - If float, then `max_features` is a fraction and\n `int(max_features * n_features)` features are considered at each\n split\n - If \"auto\", then `max_features=sqrt(n_features)`\n - If \"sqrt\", then `max_features=sqrt(n_features)`\n - If \"log2\", then `max_features=log2(n_features)`\n - If None, then `max_features=n_features`\n\n Note: the search for a split does not stop until at least one\n valid partition of the node samples is found, even if it requires to\n effectively inspect more than ``max_features`` features"},{"name":"max_leaf_nodes","data_type":"int","default_value":"null","description":"Grow a tree with ``max_leaf_nodes`` in best-first fashion\n Best nodes are defined as relative reduction in impurity\n If None then unlimited number of leaf nodes"},{"name":"min_impurity_decrease","data_type":"float","default_value":"0.0","description":"A node will be split if this split induces a decrease of the impurity\n greater than or equal to this value\n\n The weighted impurity decrease equation is the following::\n\n N_t \/ N * (impurity - N_t_R \/ N_t * right_impurity\n - N_t_L \/ N_t * left_impurity)\n\n where ``N`` is the total number of samples, ``N_t`` is the number of\n samples at the current node, ``N_t_L`` is the number of samples in the\n left child, and ``N_t_R`` is the number of samples in the right child\n\n ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n if ``sample_weight`` is passed\n\n .. versionadded:: 0.19"},{"name":"min_impurity_split","data_type":"float","default_value":"null","description":"Threshold for early stopping in tree growth. A node will split\n if its impurity is above the threshold, otherwise it is a leaf\n\n .. deprecated:: 0.19\n ``min_impurity_split`` has been deprecated in favor of\n ``min_impurity_decrease`` in 0.19. The default value of\n ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it\n will be removed in 0.25. Use ``min_impurity_decrease`` instead"},{"name":"min_samples_leaf","data_type":"int or float","default_value":"1","description":"The minimum number of samples required to be at a leaf node\n A split point at any depth will only be considered if it leaves at\n least ``min_samples_leaf`` training samples in each of the left and\n right branches. This may have the effect of smoothing the model,\n especially in regression\n\n - If int, then consider `min_samples_leaf` as the minimum number\n - If float, then `min_samples_leaf` is a fraction and\n `ceil(min_samples_leaf * n_samples)` are the minimum\n number of samples for each node\n\n .. versionchanged:: 0.18\n Added float values for fractions"},{"name":"min_samples_split","data_type":"int or float","default_value":"2","description":"The minimum number of samples required to split an internal node:\n\n - If int, then consider `min_samples_split` as the minimum number\n - If float, then `min_samples_split` is a fraction and\n `ceil(min_samples_split * n_samples)` are the minimum\n number of samples for each split\n\n .. versionchanged:: 0.18\n Added float values for fractions"},{"name":"min_weight_fraction_leaf","data_type":"float","default_value":"0.0","description":"The minimum weighted fraction of the sum total of weights (of all\n the input samples) required to be at a leaf node. Samples have\n equal weight when sample_weight is not provided"},{"name":"presort","data_type":"deprecated","default_value":"\"deprecated\"","description":"This parameter is deprecated and will be removed in v0.24\n\n .. deprecated:: 0.22"},{"name":"random_state","data_type":"int or RandomState","default_value":"null","description":"If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used\n by `np.random`"},{"name":"splitter","data_type":[],"default_value":"\"best\"","description":[]}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}},{"identifier":"step_1","flow":{"id":"17740","uploader":"12269","name":"sklearn.decomposition._kernel_pca.KernelPCA","custom_name":"sklearn.KernelPCA","class_name":"sklearn.decomposition._kernel_pca.KernelPCA","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Kernel Principal component analysis (KPCA)\n\nNon-linear dimensionality reduction through the use of kernels (see\n:ref:`metrics`).","upload_date":"2020-05-18T23:48:33","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"alpha","data_type":"int","default_value":"4","description":"Hyperparameter of the ridge regression that learns the\n inverse transform (when fit_inverse_transform=True)"},{"name":"coef0","data_type":"float","default_value":"1","description":"Independent term in poly and sigmoid kernels\n Ignored by other kernels"},{"name":"copy_X","data_type":"boolean","default_value":"false","description":"If True, input X is copied and stored by the model in the `X_fit_`\n attribute. If no further changes will be done to X, setting\n `copy_X=False` saves memory by storing a reference\n\n .. versionadded:: 0.18"},{"name":"degree","data_type":"int","default_value":"3","description":"Degree for poly kernels. Ignored by other kernels"},{"name":"eigen_solver","data_type":"string","default_value":"\"arpack\"","description":"Select eigensolver to use. If n_components is much less than\n the number of training samples, arpack may be more efficient\n than the dense eigensolver"},{"name":"fit_inverse_transform","data_type":"bool","default_value":"true","description":"Learn the inverse transform for non-precomputed kernels\n (i.e. learn to find the pre-image of a point)"},{"name":"gamma","data_type":"float","default_value":"null","description":"Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other\n kernels"},{"name":"kernel","data_type":[],"default_value":"\"cosine\"","description":[]},{"name":"kernel_params","data_type":"mapping of string to any","default_value":"null","description":"Parameters (keyword arguments) and values for kernel passed as\n callable object. Ignored by other kernels"},{"name":"max_iter","data_type":"int","default_value":"524","description":"Maximum number of iterations for arpack\n If None, optimal value will be chosen by arpack"},{"name":"n_components","data_type":"int","default_value":"4","description":"Number of components. If None, all non-zero components are kept\n\nkernel : \"linear\" | \"poly\" | \"rbf\" | \"sigmoid\" | \"cosine\" | \"precomputed\"\n Kernel. Default=\"linear\""},{"name":"n_jobs","data_type":"int or None","default_value":"1","description":"The number of parallel jobs to run\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context\n ``-1`` means using all processors. See :term:`Glossary `\n for more details\n\n .. versionadded:: 0.18"},{"name":"random_state","data_type":"int","default_value":"42","description":"If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used\n by `np.random`. Used when ``eigen_solver`` == 'arpack'\n\n .. versionadded:: 0.18"},{"name":"remove_zero_eig","data_type":"boolean","default_value":"false","description":"If True, then all components with zero eigenvalues are removed, so\n that the number of components in the output may be < n_components\n (and sometimes even zero due to numerical instability)\n When n_components is None, this parameter is ignored and components\n with zero eigenvalues are removed regardless"},{"name":"tol","data_type":"float","default_value":"0.6476998368285369","description":"Convergence tolerance for arpack\n If 0, optimal value will be chosen by arpack"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}},{"identifier":"step_0","flow":{"id":"18449","uploader":"12269","name":"sklearn.impute._base.MissingIndicator","custom_name":"sklearn.MissingIndicator","class_name":"sklearn.impute._base.MissingIndicator","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Binary indicators for missing values.\n\nNote that this component typically should not be used in a vanilla\n:class:`Pipeline` consisting of transformers and a classifier, but rather\ncould be added using a :class:`FeatureUnion` or :class:`ColumnTransformer`.","upload_date":"2020-05-21T07:48:00","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"error_on_new","data_type":"boolean","default_value":"true","description":"If True (default), transform will raise an error when there are\n features with missing values in transform that have no missing values\n in fit. This is applicable only when ``features=\"missing-only\"``."},{"name":"features","data_type":"str","default_value":"\"all\"","description":"Whether the imputer mask should represent all or a subset of\n features\n\n - If \"missing-only\" (default), the imputer mask will only represent\n features containing missing values during fit time\n - If \"all\", the imputer mask will represent all features"},{"name":"missing_values","data_type":"number","default_value":"NaN","description":"The placeholder for the missing values. All occurrences of\n `missing_values` will be indicated (True in the output array), the\n other values will be marked as False"},{"name":"sparse","data_type":"boolean or","default_value":"\"auto\"","description":"Whether the imputer mask format should be sparse or dense\n\n - If \"auto\" (default), the imputer mask will be of same type as\n input\n - If True, the imputer mask will be a sparse matrix\n - If False, the imputer mask will be a numpy array"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}