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# sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput=’uniform_average’)
from sklearn.metrics import mean_squared_error
y_true = [3, -0.5, 2, 7]
y_pred = [2.5, 0.0, 2, 8]
rmse = mean_squared_error(y_true, y_pred)**(1/2)
def rmsle(predicted,real):
sum=0.0
for x in range(len(predicted)):
p = np.log(predicted[x]+1)
r = np.log(real[x]+1)
sum = sum + (p - r)**2
return (sum/len(predicted))**0.5