{"flow":{"id":"361","uploader":"1159","name":"sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))","custom_name":"sklearn.Pipeline(StandardScaler,AdaBoostClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"2","external_version":"openml==0.12.2,sklearn==0.22.2","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":"2022-11-24T21:06:06","language":"English","dependencies":"sklearn==0.22.2\nnumpy>=1.11.0\nscipy>=0.17.0\njoblib>=0.11","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\": \"scaler\", \"step_name\": \"scaler\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"boosting\", \"step_name\": \"boosting\"}}]","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":"scaler","flow":{"id":"362","uploader":"1159","name":"sklearn.preprocessing._data.StandardScaler","custom_name":"sklearn.StandardScaler","class_name":"sklearn.preprocessing._data.StandardScaler","version":"3","external_version":"openml==0.12.2,sklearn==0.22.2","description":"Standardize features by removing the mean and scaling to unit variance\n\nThe standard score of a sample `x` is calculated as:\n\n z = (x - u) \/ s\n\nwhere `u` is the mean of the training samples or zero if `with_mean=False`,\nand `s` is the standard deviation of the training samples or one if\n`with_std=False`.\n\nCentering and scaling happen independently on each feature by computing\nthe relevant statistics on the samples in the training set. Mean and\nstandard deviation are then stored to be used on later data using\n:meth:`transform`.\n\nStandardization of a dataset is a common requirement for many\nmachine learning estimators: they might behave badly if the\nindividual features do not more or less look like standard normally\ndistributed data (e.g. Gaussian with 0 mean and unit variance).\n\nFor instance many elements used in the objective function of\na learning algorithm (such as the RBF kernel of Support Vector\nMachines or the L1 and L2 regularizers of linear models) assume that\nall features are centered around 0 a...","upload_date":"2022-11-24T21:06:06","language":"English","dependencies":"sklearn==0.22.2\nnumpy>=1.11.0\nscipy>=0.17.0\njoblib>=0.11","parameter":[{"name":"copy","data_type":"boolean","default_value":"true","description":"If False, try to avoid a copy and do inplace scaling instead\n This is not guaranteed to always work inplace; e.g. if the data is\n not a NumPy array or scipy.sparse CSR matrix, a copy may still be\n returned"},{"name":"with_mean","data_type":"boolean","default_value":"false","description":"If True, center the data before scaling\n This does not work (and will raise an exception) when attempted on\n sparse matrices, because centering them entails building a dense\n matrix which in common use cases is likely to be too large to fit in\n memory"},{"name":"with_std","data_type":"boolean","default_value":"true","description":"If True, scale the data to unit variance (or equivalently,\n unit standard deviation)."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.2"]}},{"identifier":"boosting","flow":{"id":"363","uploader":"1159","name":"sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)","custom_name":"sklearn.AdaBoostClassifier","class_name":"sklearn.ensemble._weight_boosting.AdaBoostClassifier","version":"2","external_version":"openml==0.12.2,sklearn==0.22.2","description":"An AdaBoost classifier.\n\nAn AdaBoost [1] classifier is a meta-estimator that begins by fitting a\nclassifier on the original dataset and then fits additional copies of the\nclassifier on the same dataset but where the weights of incorrectly\nclassified instances are adjusted such that subsequent classifiers focus\nmore on difficult cases.\n\nThis class implements the algorithm known as AdaBoost-SAMME [2].","upload_date":"2022-11-24T21:06:06","language":"English","dependencies":"sklearn==0.22.2\nnumpy>=1.11.0\nscipy>=0.17.0\njoblib>=0.11","parameter":[{"name":"algorithm","data_type":[],"default_value":"\"SAMME.R\"","description":[]},{"name":"base_estimator","data_type":"object","default_value":"{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"base_estimator\", \"step_name\": null}}","description":"The base estimator from which the boosted ensemble is built\n Support for sample weighting is required, as well as proper\n ``classes_`` and ``n_classes_`` attributes. If ``None``, then\n the base estimator is ``DecisionTreeClassifier(max_depth=1)``"},{"name":"learning_rate","data_type":"float","default_value":"1.0","description":"Learning rate shrinks the contribution of each classifier by\n ``learning_rate``. There is a trade-off between ``learning_rate`` and\n ``n_estimators``\n\nalgorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R')\n If 'SAMME.R' then use the SAMME.R real boosting algorithm\n ``base_estimator`` must support calculation of class probabilities\n If 'SAMME' then use the SAMME discrete boosting algorithm\n The SAMME.R algorithm typically converges faster than SAMME,\n achieving a lower test error with fewer boosting iterations"},{"name":"n_estimators","data_type":"int","default_value":"50","description":"The maximum number of estimators at which boosting is terminated\n In case of perfect fit, the learning procedure is stopped early"},{"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`."}],"component":{"identifier":"base_estimator","flow":{"id":"364","uploader":"1159","name":"sklearn.tree._classes.DecisionTreeClassifier","custom_name":"sklearn.DecisionTreeClassifier","class_name":"sklearn.tree._classes.DecisionTreeClassifier","version":"5","external_version":"openml==0.12.2,sklearn==0.22.2","description":"A decision tree classifier.","upload_date":"2022-11-24T21:06:06","language":"English","dependencies":"sklearn==0.22.2\nnumpy>=1.11.0\nscipy>=0.17.0\njoblib>=0.11","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_0.22.2"]}},"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.2"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.2"]}}