Newbie in Python and do not quite understand how I can count the average relative error of approximation by the

import pandas as pd
Import Math.
From Sklearn Import SVM
From Sklearn Import Preprocessing
DF = PD.Read_CSV ('file1.csv', ";", Header = None)
X_train = df.drop ([16,17], axis = 1)
Y_train = df [16]
test_data = pd.read_csv ('file2.csv', ";", Header = none)
X_test = test_data.drop ([16,17], axis = 1)
Y_Test = Test_Data [16]
normalized_x_train = preprocessing.normalize (x_train)
Normalized_x_Test = Preprocessing.Normalize (X_TEST)
xgb_model = svm.svr (kernel = 'linear', c = 1000.0)
CL = XGB_MODEL.FIT (Normalized_X_TRAIN, Y_TRAIN)
PREDICTIONS = Cl.Predict (Normalized_X_TEST)

Is there any finished function to get this error or only a cycle? If a cycle, then you need to normalize y_test – real values?

Answer 1, Authority 100%

You can:

from sklearn.metrics import mean_absolute_error
MAPE = Mean_absolute_error (Y_Test, Y_Predicted) / Y_Test.abs (). SUM ()

If you need percentages, then MAPE must be multiplied by 100.

PS It is also worth mentioning that this metric is rarely used in practice. It can cause division to zero.