3 Ways to Calculate the RMSE in Python


What is the RMSE?

The RMSE, or Root Mean Square Error, is a commonly used metric to measure the standard deviation of the errors. It provides a method for quantifying the difference between values predicted and observed by a model. Overall, the RMSE helps us evaluate the accuracy of a prediction model.

Calculating the RMSE in Python

While the RMSE is commonly calculated by hand, if you're looking for an easier method, we will be going over three methods to calculate the RMSE using Python.

Method #1: For Loops & Exponents

The brute force way to calculate the RMSE in Python is to use Python's in built tools, such as for loops and exponents.

Reset Code Python Output:

The calculated Root Mean Square Error (RMSE) is: 0.95.

Method #2: sklearn & math

The RMSE can also be calculated in Python using sklearn.metrics.mean_squared_error, which makes it much simpler than our previous example.

Reset Code Python Output:

The calculated Root Mean Square Error (RMSE) is: 0.95.

Method #3: numpy

Our final method involves using Python's numpy library to calculate the RMSE without the use of for loops.

Reset Code Python Output:

The calculated Root Mean Square Error (RMSE) is: 0.95.


Overall, no matter which method you choose, you will be able to calculate the RMSE in no time with the help of Python.