{"flow":{"id":"18922","uploader":"6691","name":"sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)","custom_name":"sklearn.Pipeline(Imputer,DecisionTreeClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"22","external_version":"openml==0.12.2,sklearn==0.19.0","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\nto None.","upload_date":"2021-08-14T02:45:16","language":"English","dependencies":"sklearn==0.19.0\nnumpy>=1.8.2\nscipy>=0.13.3","parameter":[{"name":"memory","data_type":"Instance of sklearn","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\": \"imputer\", \"step_name\": \"imputer\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"estimator\", \"step_name\": \"estimator\"}}]","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"}],"component":[{"identifier":"imputer","flow":{"id":"18923","uploader":"6691","name":"sklearn.preprocessing.imputation.Imputer","custom_name":"sklearn.Imputer","class_name":"sklearn.preprocessing.imputation.Imputer","version":"53","external_version":"openml==0.12.2,sklearn==0.19.0","description":"Imputation transformer for completing missing values.","upload_date":"2021-08-14T02:45:16","language":"English","dependencies":"sklearn==0.19.0\nnumpy>=1.8.2\nscipy>=0.13.3","parameter":[{"name":"axis","data_type":"integer","default_value":"0","description":"The axis along which to impute\n\n - If `axis=0`, then impute along columns\n - If `axis=1`, then impute along rows"},{"name":"copy","data_type":"boolean","default_value":"true","description":"If True, a copy of X will be created. If False, imputation will\n be done in-place whenever possible. Note that, in the following cases,\n a new copy will always be made, even if `copy=False`:\n\n - If X is not an array of floating values;\n - If X is sparse and `missing_values=0`;\n - If `axis=0` and X is encoded as a CSR matrix;\n - If `axis=1` and X is encoded as a CSC matrix."},{"name":"missing_values","data_type":"integer or","default_value":"\"NaN\"","description":"The placeholder for the missing values. All occurrences of\n `missing_values` will be imputed. For missing values encoded as np.nan,\n use the string value \"NaN\""},{"name":"strategy","data_type":"string","default_value":"\"mean\"","description":"The imputation strategy\n\n - If \"mean\", then replace missing values using the mean along\n the axis\n - If \"median\", then replace missing values using the median along\n the axis\n - If \"most_frequent\", then replace missing using the most frequent\n value along the axis"},{"name":"verbose","data_type":"integer","default_value":"0","description":"Controls the verbosity of the imputer"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.19.0"]}},{"identifier":"estimator","flow":{"id":"18924","uploader":"6691","name":"sklearn.tree.tree.DecisionTreeClassifier","custom_name":"sklearn.DecisionTreeClassifier","class_name":"sklearn.tree.tree.DecisionTreeClassifier","version":"67","external_version":"openml==0.12.2,sklearn==0.19.0","description":"A decision tree classifier.","upload_date":"2021-08-14T02:45:16","language":"English","dependencies":"sklearn==0.19.0\nnumpy>=1.8.2\nscipy>=0.13.3","parameter":[{"name":"class_weight","data_type":"dict","default_value":"null","description":"Weights associated with classes in the form ``{class_label: weight}``\n If not given, 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":"string","default_value":"\"gini\"","description":"The function to measure the quality of a split. Supported criteria are\n \"gini\" for the Gini impurity and \"entropy\" for the information gain"},{"name":"max_depth","data_type":"int or None","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 percentage 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 or None","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 and will be removed in 0.21\n Use ``min_impurity_decrease`` instead"},{"name":"min_samples_leaf","data_type":"int","default_value":"1","description":"The minimum number of samples required to be at a leaf node:\n\n - If int, then consider `min_samples_leaf` as the minimum number\n - If float, then `min_samples_leaf` is a percentage 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 percentages"},{"name":"min_samples_split","data_type":"int","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 percentage 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 percentages"},{"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":"bool","default_value":"false","description":"Whether to presort the data to speed up the finding of best splits in\n fitting. For the default settings of a decision tree on large\n datasets, setting this to true may slow down the training process\n When using either a smaller dataset or a restricted depth, this may\n speed up the training."},{"name":"random_state","data_type":"int","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":"string","default_value":"\"best\"","description":"The strategy used to choose the split at each node. Supported\n strategies are \"best\" to choose the best split and \"random\" to choose\n the best random split"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.19.0"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.19.0"]}}