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Bias = Assumptions ---> UnderfittingVariance = Sensitivity ---> OverfittingYou made it crystal clear, thank you.
Explained a boring 30+ minute lecture to an easy, consumable less than 10 minute video. Well done!
Excellent description of a bias and variance!Impatiently waiting for a new video!
Great to hear!
Extraordinary explanation, thank you!
one of the best video out there comparing bias and variance. Thanks Assembly AI
Glad you liked it!
Great Explanation
Great Explanation !!!!!!!Thank you 😁
the best video about the subject on the internet.
Great to hear, thank you!
Thank you so much for such an amazing explanation!
THanks for your efforts.
What is the difference between training the model more and introduce more data?
excellent video
Thank you!
Great
When someone this cute teaches you can't help but give your 100% attention 😂
thanks.. u are so cute❤️❤️❤️❤️❤️
Bias = Assumptions ---> Underfitting
Variance = Sensitivity ---> Overfitting
You made it crystal clear, thank you.
Explained a boring 30+ minute lecture to an easy, consumable less than 10 minute video. Well done!
Excellent description of a bias and variance!
Impatiently waiting for a new video!
Great to hear!
Extraordinary explanation, thank you!
one of the best video out there comparing bias and variance. Thanks Assembly AI
Glad you liked it!
Great Explanation
Great Explanation !!!!!!!Thank you 😁
the best video about the subject on the internet.
Great to hear, thank you!
Thank you so much for such an amazing explanation!
THanks for your efforts.
What is the difference between training the model more and introduce more data?
excellent video
Thank you!
Great
When someone this cute teaches you can't help but give your 100% attention 😂
thanks.. u are so cute❤️❤️❤️❤️❤️