def binary_log_loss(y_true, y_prob):
 y_prob = np.clip(y_prob, 1e-10, (1 - 1e-10))
 return ((- np.sum(((y_true * np.log(y_prob)) + ((1 - y_true) * np.log((1 - y_prob)))))) / y_prob.shape[0])

def binary_log_loss ( y_true , y_prob ) : y_prob = np . clip ( y_prob , 1e-10 , ( 1 - 1e-10 ) ) return ( ( - np . sum ( ( ( y_true * np . log ( y_prob ) ) + ( ( 1 - y_true ) * np . log ( ( 1 - y_prob ) ) ) ) ) ) / y_prob . shape [ 0 ] )
compute binary logistic loss for classification .