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- builtins.object
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- AdaBoostClassifier
class AdaBoostClassifier(builtins.object) |
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AdaBoostClassifier(base_estimator=None, n_estimators=50, learning_rate=1.0, random_state=None, max_depth=3, min_samples_split=2)
AdaBoost classifier.
Builds an additive model by sequentially fitting weak classifiers (default: decision stumps)
on modified versions of the data. Each subsequent classifier focuses more on samples
that were misclassified by the previous ensemble.
Uses the SAMME algorithm which supports multi-class classification.
Attributes:
base_estimator_ (object): The base estimator template used for fitting.
n_estimators (int): The maximum number of estimators at which boosting is terminated.
learning_rate (float): Weight applied to each classifier's contribution.
estimators_ (list): The collection of fitted base estimators.
estimator_weights_ (np.ndarray): Weights for each estimator.
estimator_errors_ (np.ndarray): Classification error for each estimator.
classes_ (np.ndarray): The class labels.
n_classes_ (int): The number of classes.
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Methods defined here:
- __init__(self, base_estimator=None, n_estimators=50, learning_rate=1.0, random_state=None, max_depth=3, min_samples_split=2)
- Initialize the AdaBoostClassifier.
Args:
base_estimator (object, optional): The base estimator from which the boosted ensemble is built.
Support for sample weighting is required. If None, then
the base estimator is DecisionTreeClassifier(max_depth=1).
n_estimators (int, optional): The maximum number of estimators at which boosting is terminated.
In case of perfect fit, the learning procedure is stopped early. Defaults to 50.
learning_rate (float, optional): Weight applied to each classifier's contribution. Defaults to 1.0.
random_state (int, optional): Controls the random seed given to the base estimator at each boosting iteration.
Defaults to None.
max_depth (int, optional): The maximum depth of the base estimator. Defaults to 3.
min_samples_split (int, optional): The minimum number of samples required to split an internal node
when using the default `ClassifierTree` base estimator. Defaults to 2.
- decision_function(self, X)
- Compute the decision function of X.
- fit(self, X, y)
- Build a boosted classifier from the training set (X, y).
- get_stats(self, y_true, X=None, y_pred=None, verbose=False)
- Calculate and optionally print evaluation metrics. Requires either X or y_pred.
- predict(self, X)
- Predict classes for X.
- predict_proba(self, X)
- Predict class probabilities for X.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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