Linear Regression vs Maximum Likelihood

Published: 06 August 2024
on channel: DataMListic
4,728
282

In this video, we explore why the least squares method is closely related to the Gaussian distribution. Simply put, this happens because it assumes that the errors or residuals in the data follow a normal distribution with a mean on the regression line.

Related Videos
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Why We Don't Use the Mean Squared Error (MSE) Loss in Classification:    • Why We Don't Use the Mean Squared Err...  
The Bessel's Correction:    • Why We Divide by N-1 in the Sample Va...  
Gradient Boosting with Regression Trees Explained:    • Gradient Boosting with Regression Tre...  
P-Values Explained:    • P-Values Explained | P Value Hypothes...  
Kabsch-Umeyama Algorithm:    • Kabsch-Umeyama Algorithm - How to Ali...  
Eigendecomposition Explained:    • Eigendecomposition Explained  

Follow Me
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
🐦 Twitter: @datamlistic   / datamlistic  
📸 Instagram: @datamlistic   / datamlistic  
📱 TikTok: @datamlistic   / datamlistic  

Channel Support
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
The best way to support the channel is to share the content. ;)

If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary)
► Patreon:   / datamlistic  
► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq
► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281
► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5
► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a

#svd #singularvaluedecomposition #eigenvectors #eigenvalues #linearalgebra


Watch video Linear Regression vs Maximum Likelihood online without registration, duration hours minute second in high quality. This video was added by user DataMListic 06 August 2024, don't forget to share it with your friends and acquaintances, it has been viewed on our site 4,728 once and liked it 282 people.