SECTION_SEGMENTER_MODEL¶
-
lexnlp.nlp.en.segments.titles.
SECTION_SEGMENTER_MODEL
= ExtraTreesClassifier(ccp_alpha=None, n_estimators=25, n_jobs=1)¶ An extra-trees classifier.
This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.
Read more in the User Guide.
- n_estimators : int, default=100
The number of trees in the forest.
Changed in version 0.22: The default value of
n_estimators
changed from 10 to 100 in 0.22.- criterion : {“gini”, “entropy”}, default=”gini”
- The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
- max_depth : int, default=None
- 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.
- min_samples_split : int or float, default=2
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.
Changed in version 0.18: Added float values for fractions.
- min_samples_leaf : int or float, default=1
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.
Changed in version 0.18: Added float values for fractions.
- min_weight_fraction_leaf : float, default=0.0
- 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 : {“auto”, “sqrt”, “log2”}, int or float, default=”auto”
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).
- 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.- max_leaf_nodes : int, default=None
- 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. - min_impurity_decrease : float, default=0.0
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, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.New in version 0.19.
- min_impurity_split : float, default=None
Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.
Deprecated since version 0.19:
min_impurity_split
has been deprecated in favor ofmin_impurity_decrease
in 0.19. The default value ofmin_impurity_split
has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Usemin_impurity_decrease
instead.- bootstrap : bool, default=False
- Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
- oob_score : bool, default=False
- Whether to use out-of-bag samples to estimate the generalization accuracy.
- n_jobs : int, default=None
- The number of jobs to run in parallel.
fit()
,predict()
,decision_path()
andapply()
are all parallelized over the trees.None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details. - random_state : int, RandomState, default=None
Controls 3 sources of randomness:
- the bootstrapping of the samples used when building trees
(if
bootstrap=True
) - the sampling of the features to consider when looking for the best
split at each node (if
max_features < n_features
) - the draw of the splits for each of the max_features
See Glossary for details.
- the bootstrapping of the samples used when building trees
(if
- verbose : int, default=0
- Controls the verbosity when fitting and predicting.
- warm_start : bool, default=False
- 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 the Glossary. - class_weight : {“balanced”, “balanced_subsample”}, dict or list of dicts, 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.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 with sample_weight (passed through the fit method) if sample_weight is specified.
- ccp_alpha : non-negative float, default=0.0
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 minimal_cost_complexity_pruning for details.New in version 0.22.
- max_samples : int or float, default=None
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).
New in version 0.22.
- base_estimator_ : ExtraTreesClassifier
- The child estimator template used to create the collection of fitted sub-estimators.
- estimators_ : list of DecisionTreeClassifier
- The collection of fitted sub-estimators.
- classes_ : ndarray of shape (n_classes,) or a list of such arrays
- The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).
- n_classes_ : int or list
- The number of classes (single output problem), or a list containing the number of classes for each output (multi-output problem).
- feature_importances_ : ndarray of shape (n_features,)
The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.
Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See
sklearn.inspection.permutation_importance()
as an alternative.- n_features_ : int
- The number of features when
fit
is performed. - n_outputs_ : int
- The number of outputs when
fit
is performed. - oob_score_ : float
- Score of the training dataset obtained using an out-of-bag estimate.
This attribute exists only when
oob_score
is True. - oob_decision_function_ : ndarray of shape (n_samples, n_classes)
- Decision function computed with out-of-bag estimate on the training
set. If n_estimators is small it might be possible that a data point
was never left out during the bootstrap. In this case,
oob_decision_function_ might contain NaN. This attribute exists
only when
oob_score
is True.
sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble. RandomForestClassifier : Ensemble Classifier based on trees with optimal
splits.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.[1] P. Geurts, D. Ernst., and L. Wehenkel, “Extremely randomized trees”, Machine Learning, 63(1), 3-42, 2006. >>> from sklearn.ensemble import ExtraTreesClassifier >>> from sklearn.datasets import make_classification >>> X, y = make_classification(n_features=4, random_state=0) >>> clf = ExtraTreesClassifier(n_estimators=100, random_state=0) >>> clf.fit(X, y) ExtraTreesClassifier(random_state=0) >>> clf.predict([[0, 0, 0, 0]]) array([1])