{"flow":{"id":"1232","uploader":"1159","name":"sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier)","custom_name":"sklearn.RandomizedSearchCV(RandomForestClassifier)","class_name":"sklearn.model_selection._search.RandomizedSearchCV","version":"5","external_version":"openml==0.10.1,sklearn==0.20.0","description":"Randomized search on hyper parameters.\n\nRandomizedSearchCV implements a \"fit\" and a \"score\" method.\nIt also implements \"predict\", \"predict_proba\", \"decision_function\",\n\"transform\" and \"inverse_transform\" if they are implemented in the\nestimator used.\n\nThe parameters of the estimator used to apply these methods are optimized\nby cross-validated search over parameter settings.\n\nIn contrast to GridSearchCV, not all parameter values are tried out, but\nrather a fixed number of parameter settings is sampled from the specified\ndistributions. The number of parameter settings that are tried is\ngiven by n_iter.\n\nIf all parameters are presented as a list,\nsampling without replacement is performed. If at least one parameter\nis given as a distribution, sampling with replacement is used.\nIt is highly recommended to use continuous distributions for continuous\nparameters.\n\nNote that before SciPy 0.16, the ``scipy.stats.distributions`` do not\naccept a custom RNG instance and always use the singleton RNG from\n``numpy.random`...","upload_date":"2019-10-29T16:48:51","language":"English","dependencies":"sklearn==0.20.0\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"cv","data_type":"int","default_value":"3","description":"Determines the cross-validation splitting strategy\n Possible inputs for cv are:\n\n - None, to use the default 3-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - An object to be used as a cross-validation generator\n - An iterable yielding train, test splits\n\n For integer\/None inputs, if the estimator is a classifier and ``y`` is\n either binary or multiclass, :class:`StratifiedKFold` is used. In all\n other cases, :class:`KFold` is used\n\n Refer :ref:`User Guide ` for the various\n cross-validation strategies that can be used here\n\n .. versionchanged:: 0.20\n ``cv`` default value if None will change from 3-fold to 5-fold\n in v0.22"},{"name":"error_score","data_type":"'raise' or numeric","default_value":"\"raise-deprecating\"","description":"Value to assign to the score if an error occurs in estimator fitting\n If set to 'raise', the error is raised. If a numeric value is given,\n FitFailedWarning is raised. This parameter does not affect the refit\n step, which will always raise the error. Default is 'raise' but from\n version 0.22 it will change to np.nan"},{"name":"estimator","data_type":"estimator object","default_value":"{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"estimator\", \"step_name\": null}}","description":"A object of that type is instantiated for each grid point\n This is assumed to implement the scikit-learn estimator interface\n Either estimator needs to provide a ``score`` function,\n or ``scoring`` must be passed"},{"name":"fit_params","data_type":"dict","default_value":"null","description":"Parameters to pass to the fit method\n\n .. deprecated:: 0.19\n ``fit_params`` as a constructor argument was deprecated in version\n 0.19 and will be removed in version 0.21. Pass fit parameters to\n the ``fit`` method instead"},{"name":"iid","data_type":"boolean","default_value":"\"warn\"","description":"If True, return the average score across folds, weighted by the number\n of samples in each test set. In this case, the data is assumed to be\n identically distributed across the folds, and the loss minimized is\n the total loss per sample, and not the mean loss across the folds. If\n False, return the average score across folds. Default is True, but\n will change to False in version 0.21, to correspond to the standard\n definition of cross-validation\n\n .. versionchanged:: 0.20\n Parameter ``iid`` will change from True to False by default in\n version 0.22, and will be removed in 0.24"},{"name":"n_iter","data_type":"int","default_value":"10","description":"Number of parameter settings that are sampled. n_iter trades\n off runtime vs quality of the solution"},{"name":"n_jobs","data_type":"int or None","default_value":"null","description":"Number of jobs to run in parallel\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"},{"name":"param_distributions","data_type":"dict","default_value":"{\"bootstrap\": [true, false], \"criterion\": [\"gini\", \"entropy\"], \"max_depth\": [3, null], \"max_features\": [1, 2, 3, 4]}","description":"Dictionary with parameters names (string) as keys and distributions\n or lists of parameters to try. Distributions must provide a ``rvs``\n method for sampling (such as those from scipy.stats.distributions)\n If a list is given, it is sampled uniformly"},{"name":"pre_dispatch","data_type":"int","default_value":"\"2*n_jobs\"","description":"Controls the number of jobs that get dispatched during parallel\n execution. Reducing this number can be useful to avoid an\n explosion of memory consumption when more jobs get dispatched\n than CPUs can process. This parameter can be:\n\n - None, in which case all the jobs are immediately\n created and spawned. Use this for lightweight and\n fast-running jobs, to avoid delays due to on-demand\n spawning of the jobs\n\n - An int, giving the exact number of total jobs that are\n spawned\n\n - A string, giving an expression as a function of n_jobs,\n as in '2*n_jobs'"},{"name":"random_state","data_type":"int","default_value":"42","description":"Pseudo random number generator state used for random uniform sampling\n from lists of possible values instead of scipy.stats distributions\n 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":"refit","data_type":"boolean","default_value":"true","description":"Refit an estimator using the best found parameters on the whole\n dataset\n\n For multiple metric evaluation, this needs to be a string denoting the\n scorer that would be used to find the best parameters for refitting\n the estimator at the end\n\n The refitted estimator is made available at the ``best_estimator_``\n attribute and permits using ``predict`` directly on this\n ``RandomizedSearchCV`` instance\n\n Also for multiple metric evaluation, the attributes ``best_index_``,\n ``best_score_`` and ``best_params_`` will only be available if\n ``refit`` is set and all of them will be determined w.r.t this specific\n scorer\n\n See ``scoring`` parameter to know more about multiple metric\n evaluation"},{"name":"return_train_score","data_type":"boolean","default_value":"\"warn\"","description":"If ``False``, the ``cv_results_`` attribute will not include training\n scores\n\n Current default is ``'warn'``, which behaves as ``True`` in addition\n to raising a warning when a training score is looked up\n That default will be changed to ``False`` in 0.21\n Computing training scores is used to get insights on how different\n parameter settings impact the overfitting\/underfitting trade-off\n However computing the scores on the training set can be computationally\n expensive and is not strictly required to select the parameters that\n yield the best generalization performance."},{"name":"scoring","data_type":"string","default_value":"null","description":"A single string (see :ref:`scoring_parameter`) or a callable\n (see :ref:`scoring`) to evaluate the predictions on the test set\n\n For evaluating multiple metrics, either give a list of (unique) strings\n or a dict with names as keys and callables as values\n\n NOTE that when using custom scorers, each scorer should return a single\n value. Metric functions returning a list\/array of values can be wrapped\n into multiple scorers that return one value each\n\n See :ref:`multimetric_grid_search` for an example\n\n If None, the estimator's default scorer (if available) is used"},{"name":"verbose","data_type":"integer","default_value":"0","description":"Controls the verbosity: the higher, the more messages"}],"component":{"identifier":"estimator","flow":{"id":"1227","uploader":"1159","name":"sklearn.ensemble.forest.RandomForestClassifier","custom_name":"sklearn.RandomForestClassifier","class_name":"sklearn.ensemble.forest.RandomForestClassifier","version":"5","external_version":"openml==0.10.1,sklearn==0.20.0","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":"2019-10-29T16:48:40","language":"English","dependencies":"sklearn==0.20.0\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"},{"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":"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 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":"33","description":"The number of trees in the forest\n\n .. versionchanged:: 0.20\n The default value of ``n_estimators`` will change from 10 in\n version 0.20 to 100 in version 0.22"},{"name":"n_jobs","data_type":"int or None","default_value":"null","description":"The number of jobs to run in parallel for both `fit` and `predict`\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"},{"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":"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":"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_0.20.0"]}},"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.20.0"]}}