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I am taking a look at scikit-learn's discrete SAMME implementation and came across the following logic for computing the weighted error fraction.

# Instances incorrectly classified
incorrect = y_predict != y

# Error fraction
estimator_error = np.mean(np.average(incorrect, weights=sample_weight, axis=0))

My question: Why the use of np.mean(...) and np.average(...)?


At least in the following example, the output would be unchanged if the outer np.mean(...) call were removed.

incorrect = np.array([1, 0, 0, 0, 0, 1])
weights = np.array([0.2, 0.2, 0.2, 0.1, 0.1, 0.2])

average = np.average(incorrect, axis=0, weights=weights)
mean_average = np.mean(average)

print("average:       ", average)
print("mean(average): ", mean_average)

Output:

average:        0.4
mean(average):  0.4

Since the documentation for the AdaBoostClassifer states that y should be an array like, I don't think I am missing anything about the dimensionality. But then, what am I missing?

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