Notes The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees
which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting
those parameter values. The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, max_features=n_features
and bootstrap=False, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a
deterministic behaviour during fitting, random_state has to be fixed. References [R157] Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.
Methods
apply(X) |
Apply trees in the forest to X, return leaf indices. |
decision_path(X) |
Return the decision path in the forest |
fit(X, y[, sample_weight]) |
Build a forest of trees from the training set (X, y). |
get_params([deep]) |
Get parameters for this estimator. |
predict(X) |
Predict class for X. |
predict_log_proba(X) |
Predict class log-probabilities for X. |
predict_proba(X) |
Predict class probabilities for X. |
score(X, y[, sample_weight]) |
Returns the mean accuracy on the given test data and labels. |
set_params(**params) |
Set the parameters of this estimator. |
predict(X)-
Predict class for X.
The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.
Parameters: X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix.Returns: y : array of shape = [n_samples] or [n_samples, n_outputs]
The predicted classes.
predict_log_proba(X)-
Predict class log-probabilities for X.
The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest.
Parameters: X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix.Returns: p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
predict_proba(X)-
Predict class probabilities for X.
The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.
Parameters: X : array-like or sparse matrix of shape = [n_samples, n_features]
The input samples. Internally, its dtype will be converted to
dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparsecsr_matrix.Returns: p : array of shape = [n_samples, n_classes], or a list of n_outputs
such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
score(X, y, sample_weight=None)-
Returns the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
Mean accuracy of self.predict(X) wrt. y.
From Sklearn:
http://sklearn.apachecn.org/cn/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier