[Proof] MSE = Variance + Bias²

preview_player
Показать описание
Proof that the mean squared error of an estimator is equal to the variance plus bias squared.

Thanks for watching!! ❤️

Tip Jar 👉🏻👈🏻

  1. math et al
  2. mse bias^2 variance
  3. mse
  4. variance
  5. bias
  6. mathetal
Рекомендации по теме
[Proof] MSE = 0:04:36

[Proof] MSE = Variance + Bias²

The Mean Squared 0:06:58

The Mean Squared Error of an Estimator and the Bias Variance Tradeoff

Proof that the 0:06:58

Proof that the Sample Variance is an Unbiased Estimator of the Population Variance

Mean Squared Error 0:09:16

Mean Squared Error (MSE)|| Mean Square Error equal to variance and Bias square | Statistics |

Machine Learning Fundamentals: 0:06:36

Machine Learning Fundamentals: Bias and Variance

What is an 0:17:12

What is an unbiased estimator? Proof sample mean is unbiased and why we divide by n-1 for sample var

The Binomial Distribution: 0:13:54

The Binomial Distribution: Mathematically Deriving the Mean and Variance

Why Mean Squared 0:00:58

Why Mean Squared Error is cool.

8.3 Bias-Variance Decomposition 0:30:51

8.3 Bias-Variance Decomposition of the Squared Error (L08: Model Evaluation Part 1)

Bias & Variance 0:02:28

Bias & Variance Tradeoff with MSE

Expected Value and 0:07:57

Expected Value and Variance of Discrete Random Variables

Variance Proof 1 0:01:06

Variance Proof 1 - Intro to Statistics

Variance - Clearly 0:05:59

Variance - Clearly Explained (How To Calculate Variance)

Review and intuition 0:09:44

Review and intuition why we divide by n-1 for the unbiased sample | Khan Academy

Least Square Estimators 0:09:04

Least Square Estimators - Estimator of Variance, sigma^2

Bias Variance Tradeoff 0:09:45

Bias Variance Tradeoff Explained!

Proof: Conditional Expectation 0:07:18

Proof: Conditional Expectation Minimizes MSE

Variance vs MSE 0:03:05

Variance vs MSE 🤕in simplest way Urdu🇵🇰/Hindi🇮🇳

Mean Square Error 0:16:18

Mean Square Error (Estimator of σ^2) | Simple Linear Regression

Unbiasedness vs consistency 0:04:09

Unbiasedness vs consistency of estimators - an example

Decomposition of Variability: 0:03:20

Decomposition of Variability: Sum of Squares | Statistics Tutorial

Sample Variance (s^2) 0:08:43

Sample Variance (s^2) is BIASED Estimator | Two Different Proofs

Machine Learning Lecture 0:51:13

Machine Learning Lecture 19 'Bias Variance Decomposition' -Cornell CS4780 SP17

Bias-Variance Tradeoff : 0:12:25

Bias-Variance Tradeoff : Data Science Basics

Комментарии
Автор

The first explanation I found that takes the time to expand Bias^2. Thank you!

matheusmaldaner
Автор

Thank you so much! I spent too much time trying to figure this out and this was so clear.

sammymendis
Автор

You're doing a great job, thank you.

HaykTarkhanyan
Автор

2:23 why not distributing expected value E to theta^2???

sophia
Автор

if we are given a pdf of 4 values of x with their probabilities in terms of theta, then we find an estimator for the mean theta-hat and then we find the mean square error in terms of theta (should it be in terms of theta?), how can we find if it it mean square consistent. I am unsure because n=4 for my questions so I can't see how it makes sense to consider the limit as n goes to infinity. Please could someone shed some light. Thank you

swaggy