accuracy - Binary classification accuracy

rlscore.measure.accuracy(Y, P)

Binary classification accuracy.

A performance measure for binary classification problems. Returns the fraction of correct class predictions. P[i]>0 is considered a positive class prediction and P[i]<0 negative. P[i]==0 is considered as classifier abstaining to make a decision, which incurs 0.5 errors (in contrast to 0 error for correct and 1 error for incorrect prediction).

If 2-dimensional arrays are supplied as arguments, then accuracy is separately computed for each column, after which the accuracies are averaged.

Parameters:

Y : {array-like}, shape = [n_samples] or [n_samples, n_labels]

Correct labels, must belong to set {-1,1}

P : {array-like}, shape = [n_samples] or [n_samples, n_labels]

Predicted labels, can be any real numbers.

Returns:

accuracy : float

number between 0 and 1