Oracle¶
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class
deslib.static.oracle.Oracle(pool_classifiers=None, random_state=None)[source]¶ Abstract method that always selects the base classifier that predicts the correct label if such classifier exists. This method is often used to measure the upper-limit performance that can be achieved by a dynamic classifier selection technique. It is used as a benchmark by several dynamic selection algorithms
Parameters: - pool_classifiers : list of classifiers (Default = None)
The generated_pool of classifiers trained for the corresponding classification problem. Each base classifiers should support the method “predict”. If None, then the pool of classifiers is a bagging classifier.
- random_state : int, RandomState instance or None, optional (default=None)
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.
References
Kuncheva, Ludmila I. “A theoretical study on six classifier fusion strategies.” IEEE Transactions on Pattern Analysis & Machine Intelligence, (2002): 281-286.
R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier selection: Recent advances and perspectives,” Information Fusion, vol. 41, pp. 195 – 216, 2018.
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fit(X, y)[source]¶ Fit the model according to the given training data.
Parameters: - X : array of shape = [n_samples, n_features]
Data used to fit the model.
- y : array of shape = [n_samples]
class labels of each example in X.
Returns: - self : object
Returns self.
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predict(X, y)[source]¶ Prepare the labels using the Oracle model.
Parameters: - X : array of shape = [n_samples, n_features]
The data to be classified
- y : array of shape = [n_samples]
Class labels of each sample in X.
Returns: - predicted_labels : array of shape = [n_samples]
Predicted class for each sample in X.