algorithm %s is not supported

Package:

scikit-learn

47032

Exception Class:

ValueError

Raise code

""" 
        Returns
        -------
        self : object
            Fitted estimator.
        """
        # Check that algorithm is supported
        if self.algorithm not in ('SAMME', 'SAMME.R'):
            raise ValueError("algorithm %s is not supported" % self.algorithm)

        # Fit
        return super().fit(X, y, sample_weight)

    def _validate_estimator(self):
        """Check the estimator and set the base_estimator_ attribute."""
        super()._validate_estimator(

Links to the raise (1)

https://github.com/scikit-learn/scikit-learn/blob/c67518350f91072f9d37ed09c5ef7edf555b6cf6/sklearn/ensemble/_weight_boosting.py#L440

Ways to fix

Summary:

This exception can be thrown when calling fit on an instance of AdaBoostClassifier. When creating the instance of adaBoostClassifier, you have the option to set the value of the algorithm as an optional parameter. the value of this parameter can only be one of 2 values: 'SAMME' or 'SAMME.R'. If it's set to anything else, it will throw an exception.

Code to Reproduce the Error (Wrong):

from sklearn.ensemble._weight_boosting import AdaBoostClassifier

abc = AdaBoostClassifier(algorithm='alg')
X = np.array([[1, 2], [3, 4]])
y = np.array([1, 2])
abc.fit(X, y)

Error Message:

ValueError                                Traceback (most recent call last)
<ipython-input-50-a1eb30b3bbb9> in <module>()
      4 X = np.array([[1, 2], [3, 4]])
      5 y = np.array([1, 2])
----> 6 abc.fit(X, y)

/usr/local/lib/python3.7/dist-packages/sklearn/ensemble/_weight_boosting.py in fit(self, X, y, sample_weight)
    433         # Check that algorithm is supported
    434         if self.algorithm not in ('SAMME', 'SAMME.R'):
--> 435             raise ValueError("algorithm %s is not supported" % self.algorithm)
    436 
    437         # Fit

ValueError: algorithm alg is not supported

Working Version (Right):

from sklearn.ensemble._weight_boosting import AdaBoostClassifier

abc = AdaBoostClassifier(algorithm='SAMME')
X = np.array([[1, 2], [3, 4]])
y = np.array([1, 2])
abc.fit(X, y)

Successful Output:

AdaBoostClassifier(algorithm='SAMME', base_estimator=None, learning_rate=1.0,
                   n_estimators=50, random_state=None)

Jul 16, 2021

codingcomedyig 7.9k

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