| 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 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 | 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 |