{"flow":{"id":"17901","uploader":"12269","name":"sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_1=sklearn.ensemble._forest.RandomForestClassifier)","custom_name":"sklearn.Pipeline(StackingEstimator,RandomForestClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"1","external_version":"automl==0.0.1,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":"2020-05-19T03:41:43","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\"}}]","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_1","flow":{"id":"17654","uploader":"8323","name":"sklearn.ensemble._forest.RandomForestClassifier","custom_name":"sklearn.RandomForestClassifier","class_name":"sklearn.ensemble._forest.RandomForestClassifier","version":"2","external_version":"openml==0.10.2,sklearn==0.22.1","description":"A random forest classifier.\n\nA random forest is a meta estimator that fits a number of decision tree\nclassifiers on various sub-samples of the dataset and uses averaging to\nimprove the predictive accuracy and control over-fitting.\nThe sub-sample size is always the same as the original\ninput sample size but the samples are drawn with replacement if\n`bootstrap=True` (default).","upload_date":"2020-04-05T22:13:51","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"bootstrap","data_type":"boolean","default_value":"true","description":"Whether bootstrap samples are used when building trees. If False, the\n whole datset is used to build each tree"},{"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 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 The \"balanced_subsample\" mode is the same as \"balanced\" except that\n weights are computed based on the bootstrap sample for every tree\n grown\n\n For multi-output, the weights of each column of y will be multiplied\n\n Note that these weights will be multiplied..."},{"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\n Note: this parameter is tree-specific"},{"name":"max_depth","data_type":"integer 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":"\"auto\"","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)` (same as \"auto\")\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 trees 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":"max_samples","data_type":"int or float","default_value":"null","description":"If bootstrap is True, the number of samples to draw from X\n to train each base estimator\n\n - If None (default), then draw `X.shape[0]` samples\n - If int, then draw `max_samples` samples\n - If float, then draw `max_samples * X.shape[0]` samples. Thus,\n `max_samples` should be in the interval `(0, 1)`\n\n .. versionadded:: 0.22"},{"name":"min_impurity_decrease","data_type":"float","default_value":"1e-07","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":"0","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","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","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":"n_estimators","data_type":"integer","default_value":"10","description":"The number of trees in the forest\n\n .. versionchanged:: 0.22\n The default value of ``n_estimators`` changed from 10 to 100\n in 0.22"},{"name":"n_jobs","data_type":"int or None","default_value":"1","description":"The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n :meth:`decision_path` and :meth:`apply` are all parallelized over the\n trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n context. ``-1`` means using all processors. See :term:`Glossary\n ` for more details"},{"name":"oob_score","data_type":"bool","default_value":"false","description":"Whether to use out-of-bag samples to estimate\n the generalization accuracy"},{"name":"random_state","data_type":"int","default_value":"1","description":"Controls both the randomness of the bootstrapping of the samples used\n when building trees (if ``bootstrap=True``) and the sampling of the\n features to consider when looking for the best split at each node\n (if ``max_features < n_features``)\n See :term:`Glossary ` for details"},{"name":"verbose","data_type":"int","default_value":"0","description":"Controls the verbosity when fitting and predicting"},{"name":"warm_start","data_type":"bool","default_value":"false","description":"When set to ``True``, reuse the solution of the previous call to fit\n and add more estimators to the ensemble, otherwise, just fit a whole\n new forest. See :term:`the Glossary `"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}},{"identifier":"step_0","flow":{"id":"17893","uploader":"12269","name":"automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)","custom_name":"automl.StackingEstimator","class_name":"automl.util.sklearn.StackingEstimator","version":"1","external_version":"automl==0.0.1,openml==0.10.2,sklearn==0.22.1","description":"StackingEstimator\n\nA shallow wrapper around a classification algorithm to implement the transform method. Allows stacking of\narbitrary classification algorithms in a pipelines.","upload_date":"2020-05-19T03:39:14","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":{"name":"estimator","data_type":"PredictionMixin","default_value":"{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"estimator\", \"step_name\": null}}","description":"An instance implementing PredictionMixin"},"component":{"identifier":"estimator","flow":{"id":"17705","uploader":"12269","name":"sklearn.discriminant_analysis.LinearDiscriminantAnalysis","custom_name":"sklearn.LinearDiscriminantAnalysis","class_name":"sklearn.discriminant_analysis.LinearDiscriminantAnalysis","version":"4","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Linear Discriminant Analysis\n\nA classifier with a linear decision boundary, generated by fitting class\nconditional densities to the data and using Bayes' rule.\n\nThe model fits a Gaussian density to each class, assuming that all classes\nshare the same covariance matrix.\n\nThe fitted model can also be used to reduce the dimensionality of the input\nby projecting it to the most discriminative directions.\n\n.. versionadded:: 0.17\n *LinearDiscriminantAnalysis*.","upload_date":"2020-05-18T19:46:37","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"n_components","data_type":"int","default_value":"346","description":"Number of components (<= min(n_classes - 1, n_features)) for\n dimensionality reduction. If None, will be set to\n min(n_classes - 1, n_features)"},{"name":"priors","data_type":"array","default_value":"null","description":"Class priors"},{"name":"shrinkage","data_type":"string or float","default_value":"0.4904334135320484","description":"Shrinkage parameter, possible values:\n - None: no shrinkage (default)\n - 'auto': automatic shrinkage using the Ledoit-Wolf lemma\n - float between 0 and 1: fixed shrinkage parameter\n\n Note that shrinkage works only with 'lsqr' and 'eigen' solvers"},{"name":"solver","data_type":"string","default_value":"\"eigen\"","description":"Solver to use, possible values:\n - 'svd': Singular value decomposition (default)\n Does not compute the covariance matrix, therefore this solver is\n recommended for data with a large number of features\n - 'lsqr': Least squares solution, can be combined with shrinkage\n - 'eigen': Eigenvalue decomposition, can be combined with shrinkage"},{"name":"store_covariance","data_type":"bool","default_value":"false","description":"Additionally compute class covariance matrix (default False), used\n only in 'svd' solver\n\n .. versionadded:: 0.17"},{"name":"tol","data_type":"float","default_value":"0.0012994132677570124","description":"Threshold used for rank estimation in SVD solver\n\n .. versionadded:: 0.17"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}},"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}