Underfitting & Overfitting - Explained

Underfitting and overfitting are some of the most common problems you encounter while constructing a statistical/machine learning model. It is therefore important to be able to recognize when either is occurring and what can be done to fix it. This is an extremely brief overview covering those topics, and, as always in machine learning, there is more to learn.
Resources for Further Study:
More in-depth look at fixing underfitting and overfitting
towardsdatasci....
(Note: this author states that more data will not help with underfitting, however this is mainly true when you already have a bad model. If you are working with significantly less data (100s or 1000s of data points) then getting more data will make a bigger impact on underfitting.)
A full example of underfitting and overfitting
-towardsdatasci...
Music:
Äitienpäivä '22 by Brylie Christopher Oxley, brylie.bandcam...,
licensed under CC BY 4.0
Goldberg Variations, BWV 988 - 26 - Variatio 25 a 2 Clav., used from Public Domain

[@faiyazislam8549]

Bro please keep up with the videos your explanations are spot on!

[@VictorYarema]

Wow. Your explanation is incredibly concise. Thanks!

[@Ms-money]

very nicely explained!

[@sanyudsouza4153]

perfect calming bg music choice tbh

[@sillyproofs]

Makes sense..

[@Tabu11211]

Subscribed

[@amanxo1]

CHEERS MATE

[@ThatisnotHair]

This is what sciéΠcë is