Machine Learning Model Evaluation Metrics

[@kannan3801]

1.Classification -> 1:50
a.Accuracy : 2:32
b.Confusion Matrix : 4:37
c.Precision : 7:39
d.Recall : 8:21
e.F1 score : 8:41
2.Regression -> 24:44
a.R^2 : 25:13
b.MAE : 27:42
c.MSE : 28:07
d.RMSE : 28:22
e.RMSLE : 31:31
f.MAE VS RMSE : 29:10

[@diegosolis9681]

She was really nervous, and still could manage to give an excelente presentation filled with knowledge. That shows how much she actually know about the topic. Outstanding!

[@petrosiliadis5461]

Classification metrics -> 1:50
Regression metrics ->24:43

[@adamyatripathi2743]

Seemed a little nervous, but she nailed it. Amazing content and presentation!!

[@karakol86]

This material is available on her blog under her "Posts" section. One of the best summaries I have seen.

[@mimonwimoo4937]

great presentation! I wish my teacher explained like that... I learned so much from this, thank you!

[@imagenigraphics]

I can feel the tension but I understood what I was trying to learn. Thank you.

[@kccchiu]

Great content.
I knew is gonna be a great presentation looking at her laptop.

[@sanwalyousaf6405]

This is such an interesting and engaging talk. Kudos

[@mikesmith1611]

Great summary 👍🏼

[@ximo5628]

This is so great. Thanks!

[@PanMarhewa]

Looks like she was hardly stressed, but managed very well

[@KenoKanawa]

Helped me thank you

[@pakjuaan]

Thank you very much emma stone

[@melissaznf1665]

Amazing content

[@ThiagoVieiratcvieira]

love it!

[@MegaThundergaming]

5:34 🙃👍

[@arjungoud3450]

Better listen to every word she says, everything is informative

[@lqtube]

nervous but great content

[@aryamahima3]

Great presentation...so well explained..however she seems disinterested in explaining..but thanks for the video..