The Mean Squared Error measures how close a regression line is to a set of data points. The Most Comprehensive Guide for Beginners on What Is Correlation Lesson - 24

Your Best Guide to Understand Correlation vs. The Complete Guide to Understand Pearson's Correlation Lesson - 20Ī Complete Guide on the Types of Statistical Studies Lesson - 21Įverything You Need to Know About Poisson Distribution Lesson - 22 The Complete Guide to Skewness and Kurtosis Lesson - 15Ī Holistic Look at Bernoulli Distribution Lesson - 16Īll You Need to Know About Bias in Statistics Lesson - 17Ī Complete Guide to Get a Grasp of Time Series Analysis Lesson - 18 The Definitive Guide to Understand Spearman’s Rank Correlation Lesson - 12Ī Comprehensive Guide to Understand Mean Squared Error Lesson - 13Īll You Need to Know About the Empirical Rule in Statistics Lesson - 14 Understanding the Fundamentals of Arithmetic and Geometric Progression Lesson - 11 The Best Guide to Understand Bayes Theorem Lesson - 6Įverything You Need to Know About the Normal Distribution Lesson - 7Īn In-Depth Explanation of Cumulative Distribution Function Lesson - 8Ī Complete Guide to Chi-Square Test Lesson - 9Ī Complete Guide on Hypothesis Testing in Statistics Lesson - 10 The Ultimate Guide to Understand Conditional Probability Lesson - 4Ī Comprehensive Look at Percentile in Statistics Lesson - 5 The Best Guide to Understand Central Limit Theorem Lesson - 2Īn In-Depth Guide to Measures of Central Tendency : Mean, Median and Mode Lesson - 3 RMSE is equal to the square root of MSE.Everything You Need to Know About the Probability Density Function in Statistics Lesson - 1 Computing root mean squared error (RMSE) using scikit-learn In contrast, RMSE can only be loosely defined as so, with the added interpretation that higher error terms are penalised more.

MAE can be defined as the average absolute differences between the actual and predicted values. This is because MAE involves taking the absolute value, which is not easily differentiable compared to RMSE. RMSE is often used in loss functions that require optimisation through standard techniques such as gradient descent, while MAE is not. This is why we should be cautious when comparing RMSE derived from two different sample sizes. This means that as the number of samples increase, the upper bound of RMSE increases.

Mean squared error