6

Absolute Error

Unsolved
Prob. and Stats

Difficulty: 2 | Problem written by mesakarghm
Problem reported in interviews at     If x is the actual value of a quantity and x0 is the predicted value, then the absolute error can be calculated using the formula:

$$\delta x = x_0 - x$$

For multiple predictions, the arithmetic mean of absolute errors of individual measurements should be the final absolute error.

$$\delta x = {\sum |x_0 - x| \over n}$$

x0 is the predicted value

x is the actual value

and n is the length of array

For a given set of arrays (two numeric lists of same length with actual value as the first list and predicted value as the second list), calculate and return the absolute error.

<class 'list'>
arr1: [1, 2, 3]
<class 'list'>
arr2: [3, 4, 5]

Expected Output:
<class 'float'>
2.0

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uahnbu • 8 months, 1 week ago

1

• Using list operators:
return (sum(arr2) - sum(arr1)) / len(arr2)
• Using numpy:

Note: You must manually import np although it says that np has been imported, as np is only automatically imported in tests.

return np.average(np.subtract(arr2, arr1))

0

Thank you for that! We have added an automatic import statement to this problem.

tien • 8 months ago

1

def abs_error(arr1,arr2):
return np.average(np.abs(np.subtract(arr1, arr2)))

It said the code passed 3/4 tests. What is the fourth one ?

trungle98hn@gmail.com • 8 months ago

0

I got same error when using numpy and traditional for loop :(

admin • 7 months, 4 weeks ago

0

Thank you for letting us know. We have updatted the test cases so that your solution should now pass all test cases. Please feel free to reach out if there is anything else that shows up!

admin • 7 months, 4 weeks ago

0

Thank you for letting us know. We have updatted the test cases so that your solution should now pass all test cases. Please feel free to reach out if there is anything else that shows up!

shandytp • 7 months ago

0

I still got the same error message, only 3/4 cases passed

abhishek_kumar • 3 months, 1 week ago

0

import numpy as np

def abs_error(arr1,arr2):
abs_diff = np.absolute(np.asarray(arr1) - np.asarray(arr2))
result = sum(abs_diff)/len(arr1)
return result

Steps:

1. Get the Absolute difference

2. Compute the average

3. return the average

REFERENCE:

harish9 • 1 month, 2 weeks ago

0

I am getting this message "Test case failure: 3 out of 4 cases passed.". What is the fourth case? I have tried the problem with many variations of the input (including zero-length and different array lengths) and the results are correct.

gideon_fadele • 1 month, 2 weeks ago

0

Have you tried a case where the difference between the input is negative