Visibility: public Uploaded 10-01-2024 by Continuous Integration sklearn==1.3.2 numpy>=1.17.3 scipy>=1.5.0 joblib>=1.1.1 threadpoolctl>=2.0.0 0 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_1.3.2
Issue #Downvotes for this reason By

Loading wiki
Help us complete this description Edit
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the `max_samples` parameter if `bootstrap=True` (default), otherwise the whole dataset is used to build each tree. For a comparison between tree-based ensemble models see the example :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`.


bootstrapWhether bootstrap samples are used when building trees. If False, the whole dataset is used to build each treedefault: true
ccp_alphaComplexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details .. versionadded:: 0.22default: 0.0
class_weightdefault: null
criteriondefault: "gini"
max_depthThe maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samplesdefault: null
max_featuresdefault: "sqrt"
max_leaf_nodesGrow trees with ``max_leaf_nodes`` in best-first fashion Best nodes are defined as relative reduction in impurity If None then unlimited number of leaf nodesdefault: null
max_samplesIf bootstrap is True, the number of samples to draw from X to train each base estimator - If None (default), then draw `X.shape[0]` samples - If int, then draw `max_samples` samples - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus, `max_samples` should be in the interval `(0.0, 1.0]` .. versionadded:: 0.22default: null
min_impurity_decreaseA node will be split if this split induces a decrease of the impurity greater than or equal to this value The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed .. versionadded:: 0.19default: 0.0
min_samples_leafThe minimum number of samples required to be at a leaf node A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression - If int, then consider `min_samples_leaf` as the minimum number - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node .. versionchanged:: 0.18 Added float values for fractionsdefault: 1
min_samples_splitThe minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split .. versionchanged:: 0.18 Added float values for fractionsdefault: 2
min_weight_fraction_leafThe minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided max_features : {"sqrt", "log2", None}, int or float, default="sqrt" The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split - If float, then `max_features` is a fraction and `max(1, int(max_features * n_features_in_))` features are considered at each split - If "sqrt", then `max_features=sqrt(n_features)` - If "log2", then `max_features=log2(n_features)` - If None, then `max_features=n_features` .. versionchanged:: 1.1 The default of `max_features` changed from `"auto"` to `"sqrt"` Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` featuresdefault: 0.0
n_estimatorsThe number of trees in the forest .. versionchanged:: 0.22 The default value of ``n_estimators`` changed from 10 to 100 in 0.22 criterion : {"gini", "entropy", "log_loss"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "log_loss" and "entropy" both for the Shannon information gain, see :ref:`tree_mathematical_formulation` Note: This parameter is tree-specificdefault: 5
n_jobsThe number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more detailsdefault: null
oob_scoreWhether to use out-of-bag samples to estimate the generalization score By default, :func:`~sklearn.metrics.accuracy_score` is used Provide a callable with signature `metric(y_true, y_pred)` to use a custom metric. Only available if `bootstrap=True`default: false
random_stateControls both the randomness of the bootstrapping of the samples used when building trees (if ``bootstrap=True``) and the sampling of the features to consider when looking for the best split at each node (if ``max_features < n_features``) See :term:`Glossary ` for detailsdefault: null
verboseControls the verbosity when fitting and predictingdefault: 0
warm_startWhen set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`Glossary ` and :ref:`gradient_boosting_warm_start` for details class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, default=None Weights associated with classes in the form ``{class_label: weight}`` If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}] The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class freq...default: false


List all runs
Rendering chart
Rendering table