How do we deal with outliers in data science? My Patreon : www.patreon.com/user?u=49277905
Жүктеу.....
Пікірлер: 20
@Sams3dsReviews3 жыл бұрын
If that 2-second intro music doesn't hype you up, I don't know what will 🔥
@ritvikmath
3 жыл бұрын
haha!
@hameddadgour Жыл бұрын
Great summary! Thank you.
@riccardoformenti43323 жыл бұрын
Amazing work!!
@arontapai55863 жыл бұрын
Awesome explanation!
@stuartrharder80573 жыл бұрын
Thank you for this presentation. I am a retired behavior analyst and my primary data was always the count of behavior per unit time. I often encountered days on which the learner performed well above what I expected or well below expectations. High points made me question my instruction, expectations, and instructional materials (e.g., a 3rd-grade reading passage accidentally got included among 6th-grade passages). Low points, poorer performance always suggested a bad start to the school day or feeling ill. As you said, it is very bad practice to throw away data in the interest of saving your idea of 'correctness.' Each outlier must be examined and explained.
@BhuvaneshSrivastava3 жыл бұрын
This is exactly what would help people with dealing with outliers👍 If you can show us a coding example where keeping an outlier intelligently for training would make more sense than just dropping it then that will be great! For now, I am assuming that if a data has too many outliers and you want to incorporate those in your model then better is to use a tree based model (you can correct me if I am wrong). Thank you. 😀
@zhixiangwang71652 жыл бұрын
really great lecture!!!
@JainmiahSk3 жыл бұрын
Nice video 👍 it. Can you do videos on recommendation system.
@knowledgekumar36233 жыл бұрын
I am someone who cont learn things ,if dont not understand intuition behind that.i feel you are too of my type . i love your videos:) .keep it up .......if possible ,please post some statistics concept videos, which are necessary for DS . thanks ..
@reshmithramesh7110 Жыл бұрын
Wonderful
@jonl59053 жыл бұрын
“Btw, I got this really cool lobster hat for Christmas, hope you like it” 😂
@treelight17073 жыл бұрын
Interesting for me, I am dealing with that problem right now. I was hoping you would've gone through the methods for outlier detection/classification; One class SVM, random forests, ... Thanks anyways.
@zhixiangwang71652 жыл бұрын
Awesome!
@brandoncyoung6 ай бұрын
Having domain knowledge is a must or at least someone you can consult somone on it if you dont know.
@TheMarComplex2 жыл бұрын
Thanks!
@ritvikmath
2 жыл бұрын
No problem!
@munafdamani62333 жыл бұрын
Sir today I have seen many of your video. Thank you for sharing beautiful knowledge. Q: currently preparing time series model for Nifty using option chain data. Taking open interest, premium value and volumes data at every 5 minutes. Repeative failure while normalizing the data since the price changes, some times the strike price also changes which creats outliers. How to deal with this issue. Please guide.
@harshads8853 жыл бұрын
Amazing videos Ritvik. Just a nitpick, The second strategy should be winsorizing..not seen the term "windsoring" used.
@pratik64474 ай бұрын
Hi @ritvikmath, are you also planning to make series on Applied machine learning algorithms with the intuition and mathematics behind it? >
Пікірлер: 20
If that 2-second intro music doesn't hype you up, I don't know what will 🔥
@ritvikmath
3 жыл бұрын
haha!
Great summary! Thank you.
Amazing work!!
Awesome explanation!
Thank you for this presentation. I am a retired behavior analyst and my primary data was always the count of behavior per unit time. I often encountered days on which the learner performed well above what I expected or well below expectations. High points made me question my instruction, expectations, and instructional materials (e.g., a 3rd-grade reading passage accidentally got included among 6th-grade passages). Low points, poorer performance always suggested a bad start to the school day or feeling ill. As you said, it is very bad practice to throw away data in the interest of saving your idea of 'correctness.' Each outlier must be examined and explained.
This is exactly what would help people with dealing with outliers👍 If you can show us a coding example where keeping an outlier intelligently for training would make more sense than just dropping it then that will be great! For now, I am assuming that if a data has too many outliers and you want to incorporate those in your model then better is to use a tree based model (you can correct me if I am wrong). Thank you. 😀
really great lecture!!!
Nice video 👍 it. Can you do videos on recommendation system.
I am someone who cont learn things ,if dont not understand intuition behind that.i feel you are too of my type . i love your videos:) .keep it up .......if possible ,please post some statistics concept videos, which are necessary for DS . thanks ..
Wonderful
“Btw, I got this really cool lobster hat for Christmas, hope you like it” 😂
Interesting for me, I am dealing with that problem right now. I was hoping you would've gone through the methods for outlier detection/classification; One class SVM, random forests, ... Thanks anyways.
Awesome!
Having domain knowledge is a must or at least someone you can consult somone on it if you dont know.
Thanks!
@ritvikmath
2 жыл бұрын
No problem!
Sir today I have seen many of your video. Thank you for sharing beautiful knowledge. Q: currently preparing time series model for Nifty using option chain data. Taking open interest, premium value and volumes data at every 5 minutes. Repeative failure while normalizing the data since the price changes, some times the strike price also changes which creats outliers. How to deal with this issue. Please guide.
Amazing videos Ritvik. Just a nitpick, The second strategy should be winsorizing..not seen the term "windsoring" used.
Hi @ritvikmath, are you also planning to make series on Applied machine learning algorithms with the intuition and mathematics behind it? >