Continuing in our series on anomaly detection, let's build off the last video on k-nearest neighbors and talk about another common technique, the local outlier factor. All in under 5 minutes of course!
Пікірлер: 14
@aryashahdi27902 ай бұрын
salute to this dude for the clarity of his explanations
@Anjali.Jivani5 ай бұрын
Amazing way of explaining
@kaynkayn98709 ай бұрын
Somehow you make these videos extremely informative in only 5 minutes. What a legend.
@mahmoudel-bahnasawi28094 ай бұрын
This series is truly unique; please keep it going.
@baruite3 ай бұрын
Tellement bien expliquée! merci
@AynazAbdollahzadeh2 ай бұрын
I was super lost thanks for explaining it amazingly!
@CP-tq1ue16 күн бұрын
Thanks!
@space-time-somdeep6 ай бұрын
Please continue the series sir
@tehreemqasim22046 ай бұрын
Very well explained thank you
@riccardorossi52248 ай бұрын
Hi, I wanted to ask you a question. I understood your reasoning by comparing circles and indicating as an outlier if the point of my observation is larger than that of its neighbors. But in reality it is wrong to say that it is an outlier because it has a higher density than the density of its neighbors. High density means he has samples very close to him, low density means he has samples very far from him. Therefore, the sample that is very far from the other samples, and therefore has a lower density, is an outlier. Tell me if you understand what I mean, if you can correct me you'll do me a favor.
@AricLaBarr8 ай бұрын
No problem at all! In reality the circles represent the opposite of density and more reachability. So the larger the circles mean the larger the reachability (inverse of density). That is what makes the large circles more likely to be outliers! Hope this helps!
@riccardorossi52248 ай бұрын
@@AricLaBarr Okey, Thanks again.
@chrismawata87554 ай бұрын
At 1:50 the density is defined as the inverse of the average reachability ... Somehow the 'inverse' was ignored after that which flips the meaning of density after that point.