Everything About ___
Live briefing

Everything about Mean squared error

Measure of the error of an estimator

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the true value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive is because of randomness or because the estimator does not account for information that could produce a more accurate estimate. In machine learning, specifically empirical risk minimization, MSE may refer to the empirical risk, as an estimate of the true MSE. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Mean squared errorA 5-minute orientation
  2. Skim a recent paper: Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or notInfluential work since 2022
  3. Pick up a classic book: A root-mean-square pressure fluctuations model for internal flow applicationsThe most-reprinted book on the subject
  4. Read the landmark paper: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performanceCited 5,965 times
  5. Go sideways: Bias of an estimatorA closely related topic
  6. Go sideways: Cramér–Rao boundA closely related topic

đź“– Definitions

No dictionary entry for this exact phrase.

🗂️ The subject at a glance

  1. Definition and basic properties
  2. In regression
  3. Examples
  4. Interpretation
  5. Applications
  6. Loss function

🏆 Landmark papers (most cited)

🆕 Influential since 2022

đź“š Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

đź”— Related topics