Dataford
Interview QuestionsInterview GuidesExperiencesMock InterviewsPricing
Get started

Python RMSE Function

EasyPython00:00
Practice interviewer
In session
5 left
00:00

Your question is Python RMSE Function. Start with the requirements on the right.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

You need to log in / sign up to run or submit.

Problem

Waabi evaluates prediction accuracy by comparing predicted driving values with observed values. Given two sequences, calculate their root mean square error (RMSE).

RMSE is defined as:

RMSE = sqrt(sum((predicted[i] - actual[i])²) / n)

Implement rmse(predicted, actual) and return the result as a floating-point number.

Formal Specification

  • Input: predicted, a non-empty list of numbers, and actual, a non-empty list of numbers.
  • Both lists have the same length n.
  • Output: A float representing the RMSE between corresponding values.
  • The input lists must not be modified.

Constraints

  • 1 <= len(predicted) = len(actual) <= 10^5
  • Each value is an integer or floating-point number in [-10^9, 10^9]
  • The input lists are non-empty and have equal length
  • The input lists must not be modified

Function Signature

def rmse(predicted, actual):
Your solutionPython 3
You need to log in / sign up to run or submit.
Run your code to see test output