{"flow":{"id":"42","uploader":"1159","name":"TESTddf5d47f66sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)","custom_name":"sklearn.RandomizedSearchCV(RandomForestClassifier)","class_name":"sklearn.model_selection._search.RandomizedSearchCV","version":"1","external_version":"openml==0.14.1,sklearn==1.3.2","description":"Randomized search on hyper parameters.\n\nRandomizedSearchCV implements a \"fit\" and a \"score\" method.\nIt also implements \"score_samples\", \"predict\", \"predict_proba\",\n\"decision_function\", \"transform\" and \"inverse_transform\" if they are\nimplemented in the estimator 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.","upload_date":"2024-01-10T13:48:58","language":"English","dependencies":"sklearn==1.3.2\nnumpy>=1.17.3\nscipy>=1.5.0\njoblib>=1.1.1\nthreadpoolctl>=2.0.0","parameter":[{"name":"cv","data_type":"int","default_value":"{\"oml-python:serialized_object\": \"cv_object\", \"value\": {\"name\": \"sklearn.model_selection._split.StratifiedKFold\", \"parameters\": {\"n_splits\": \"2\", \"random_state\": \"null\", \"shuffle\": \"true\"}}}","description":"Determines the cross-validation splitting strategy\n Possible inputs for cv are:\n\n - None, to use the default 5-fold cross validation,\n - integer, to specify the number of folds in a `(Stratified)KFold`,\n - :term:`CV splitter`,\n - An iterable yielding (train, test) splits as arrays of indices\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. These splitters are instantiated\n with `shuffle=False` so the splits will be the same across calls\n\n Refer :ref:`User Guide ` for the various\n cross-validation strategies that can be used here\n\n .. versionchanged:: 0.22\n ``cv`` default value if None changed from 3-fold to 5-fold"},{"name":"error_score","data_type":"'raise' or numeric","default_value":"NaN","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"},{"name":"estimator","data_type":"estimator object","default_value":"{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"estimator\", \"step_name\": null}}","description":"An 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":"n_iter","data_type":"int","default_value":"5","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","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\n\n .. versionchanged:: v0.20\n `n_jobs` default changed from 1 to None"},{"name":"param_distributions","data_type":"dict or list of dicts","default_value":"{\"bootstrap\": [true, false], \"criterion\": [\"gini\", \"entropy\"], \"max_depth\": [3, null], \"max_features\": [1, 2, 3, 4], \"min_samples_leaf\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"min_samples_split\": [2, 3, 4, 5, 6, 7, 8, 9, 10]}","description":"Dictionary with parameters names (`str`) 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\n If a list of dicts is given, first a dict is sampled uniformly, and\n then a parameter is sampled using that dict as above"},{"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 str, giving an expression as a function of n_jobs,\n as in '2*n_jobs'"},{"name":"random_state","data_type":"int","default_value":"null","description":"Pseudo random number generator state used for random uniform sampling\n from lists of possible values instead of scipy.stats distributions\n Pass an int for reproducible output across multiple\n function calls\n See :term:`Glossary `"},{"name":"refit","data_type":"bool","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 `str` denoting the\n scorer that would be used to find the best parameters for refitting\n the estimator at the end\n\n Where there are considerations other than maximum score in\n choosing a best estimator, ``refit`` can be set to a function which\n returns the selected ``best_index_`` given the ``cv_results``. In that\n case, the ``best_estimator_`` and ``best_params_`` will be set\n according to the returned ``best_index_`` while the ``best_score_``\n attribute will not be available\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 speci..."},{"name":"return_train_score","data_type":"bool","default_value":"false","description":"If ``False``, the ``cv_results_`` attribute will not include training\n scores\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\n\n .. versionadded:: 0.19\n\n .. versionchanged:: 0.21\n Default value was changed from ``True`` to ``False``"},{"name":"scoring","data_type":"str","default_value":"null","description":"Strategy to evaluate the performance of the cross-validated model on\n the test set\n\n If `scoring` represents a single score, one can use:\n\n - a single string (see :ref:`scoring_parameter`);\n - a callable (see :ref:`scoring`) that returns a single value\n\n If `scoring` represents multiple scores, one can use:\n\n - a list or tuple of unique strings;\n - a callable returning a dictionary where the keys are the metric\n names and the values are the metric scores;\n - a dictionary with metric names as keys and callables a values\n\n See :ref:`multimetric_grid_search` for an example\n\n If None, the estimator's score method is used"},{"name":"verbose","data_type":"int","default_value":"0","description":"Controls the verbosity: the higher, the more messages"}],"component":{"identifier":"estimator","flow":{"id":"43","uploader":"1159","name":"TESTddf5d47f66sklearn.ensemble._forest.RandomForestClassifier","custom_name":"sklearn.RandomForestClassifier","class_name":"sklearn.ensemble._forest.RandomForestClassifier","version":"1","external_version":"openml==0.14.1,sklearn==1.3.2","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 controlled with the `max_samples` parameter if\n`bootstrap=True` (default), otherwise the whole dataset is used to build\neach tree.\n\nFor a comparison between tree-based ensemble models see the example\n:ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.","upload_date":"2024-01-10T13:48:58","language":"English","dependencies":"sklearn==1.3.2\nnumpy>=1.17.3\nscipy>=1.5.0\njoblib>=1.1.1\nthreadpoolctl>=2.0.0","parameter":[{"name":"bootstrap","data_type":"bool","default_value":"true","description":"Whether bootstrap samples are used when building trees. If False, the\n whole dataset 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":[],"default_value":"null","description":[]},{"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":[],"default_value":"\"sqrt\"","description":[]},{"name":"max_leaf_nodes","data_type":"int","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(round(n_samples * max_samples), 1)` samples. Thus,\n `max_samples` should be in the interval `(0.0, 1.0]`\n\n .. versionadded:: 0.22"},{"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_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\n\nmax_features : {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"\n 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 `max(1, int(max_features * n_features_in_))` features are considered at each\n split\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 .. versionchanged:: 1.1\n The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`\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":"n_estimators","data_type":"int","default_value":"5","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\n\ncriterion : {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"\n The function to measure the quality of a split. Supported criteria are\n \"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the\n Shannon information gain, see :ref:`tree_mathematical_formulation`\n Note: This parameter is tree-specific"},{"name":"n_jobs","data_type":"int","default_value":"null","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 or callable","default_value":"false","description":"Whether to use out-of-bag samples to estimate the generalization score\n By default, :func:`~sklearn.metrics.accuracy_score` is used\n Provide a callable with signature `metric(y_true, y_pred)` to use a\n custom metric. Only available if `bootstrap=True`"},{"name":"random_state","data_type":"int","default_value":"null","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:`Glossary ` and\n :ref:`gradient_boosting_warm_start` for details\n\nclass_weight : {\"balanced\", \"balanced_subsample\"}, dict or list of dicts, default=None\n 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 freq..."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.3.2"]}},"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.3.2"]}}