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sklearn.ensemble.forest.RandomForestClassifier

Visibility: public Uploaded 13-08-2021 by Sergey Redyuk
sklearn==0.18
numpy>=1.6.1
scipy>=0.9 19 runs

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bootstrap | Whether bootstrap samples are used when building trees | default: true |

class_weight | "balanced_subsample" or None, optional (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 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 with sample_weight (passed through the fit method) if sample_weight is specified. | 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 percentage 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 |

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 .. versionadded:: 0.18 | default: 1e-07 |

min_samples_leaf | The minimum number of samples required to be at a leaf node: - If int, then consider `min_samples_leaf` as the minimum number - If float, then `min_samples_leaf` is a percentage and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node .. versionchanged:: 0.18 Added float values for percentages | 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 percentage and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split .. versionchanged:: 0.18 Added float values for percentages | default: 2 |

min_weight_fraction_leaf | The minimum weighted fraction of the input samples required to be at a leaf node | default: 0.0 |

n_estimators | The number of trees in the forest | default: 10 |

n_jobs | The number of jobs to run in parallel for both `fit` and `predict` If -1, then the number of jobs is set to the number of cores | default: 1 |

oob_score | Whether to use out-of-bag samples to estimate the generalization accuracy | default: false |

random_state | If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random` | default: null |

verbose | Controls the verbosity of the tree building process | 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 | default: false |

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