Machine Learning Model Evaluation Metrics
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- čas přidán 25. 07. 2024
- MARIA KHALUSOVA | DEVELOPER ADVOCATE AT JETBRAINS
Choosing the right evaluation metric for your machine learning project is crucial, as it decides which model you’ll ultimately use. Those coming to ML from software development are often self-taught, but practice exercises and competitions generally dictate the evaluation metric. In a real-world scenario, how do you choose an appropriate metric? This talk will explore the important evaluation metrics used in regression and classification tasks, their pros and cons, and how to make a smart decision. - Věda a technologie
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!
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
Classification metrics -> 1:50
Regression metrics ->24:43
Seemed a little nervous, but she nailed it. Amazing content and presentation!!
This material is available on her blog under her "Posts" section. One of the best summaries I have seen.
Great content.
I knew is gonna be a great presentation looking at her laptop.
great presentation! I wish my teacher explained like that... I learned so much from this, thank you!
I can feel the tension but I understood what I was trying to learn. Thank you.
This is so great. Thanks!
Great summary 👍🏼
love it!
This is such an interesting and engaging talk. Kudos
Helped me thank you
Thank you very much emma stone
Looks like she was hardly stressed, but managed very well
Amazing content
5:34 🙃👍
nervous but great content
Better listen to every word she says, everything is informative
Great presentation...so well explained..however she seems disinterested in explaining..but thanks for the video..