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TEST3d2db0e540sklearn.ensemble._forest.RandomForestClassifier

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sklearn==1.3.2
numpy>=1.17.3
scipy>=1.5.0
joblib>=1.1.1
threadpoolctl>=2.0.0 0 runs

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Issue | #Downvotes for this reason | By |
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bootstrap | Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree | default: true |

ccp_alpha | Complexity 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:: 0.22 | default: 0.0 |

class_weight | default: null | |

criterion | default: "gini" | |

max_depth | The 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 samples | default: null |

max_features | default: "sqrt" | |

max_leaf_nodes | Grow 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 nodes | default: null |

max_samples | If 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:: 0.22 | default: null |

min_impurity_decrease | A 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:: 0.19 | default: 0.0 |

min_samples_leaf | The 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 fractions | default: 1 |

min_samples_split | The 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 fractions | default: 2 |

min_weight_fraction_leaf | The 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`` features | default: 0.0 |

n_estimators | The 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-specific | default: 5 |

n_jobs | The 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
| default: null |

oob_score | Whether 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_state | Controls 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 | default: null |

verbose | Controls the verbosity when fitting and predicting | default: 0 |

warm_start | When 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 | default: false |

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