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sklearn.ensemble._forest.RandomForestClassifier

Visibility: public Uploaded 21-05-2021 by pedro sampaio
sklearn==0.22.2.post1
numpy>=1.11.0
scipy>=0.17.0
joblib>=0.11 4 runs

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bootstrap | Whether bootstrap samples are used when building trees. If False, the whole datset 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 | 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 frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` The "balanced_subsample" mode is the same as "balanced" except that weights are computed based on the bootstrap sample for every tree grown For multi-output, the weights of each column of y will be multiplied Note that these weights will be multiplied... | default: null |

criterion | The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain Note: this parameter is tree-specific | 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 | 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 `int(max_features * n_features)` features are considered at each split - If "auto", then `max_features=sqrt(n_features)` - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto") - If "log2", then `max_features=log2(n_features)` - If None, then `max_features=n_features` 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: "auto" |

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_samples * X.shape[0]` samples. Thus, `max_samples` should be in the interval `(0, 1)` .. 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_impurity_split | Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead | default: null |

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 | 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 | default: 100 |

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 accuracy | 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:`the Glossary | default: false |

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