StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
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- เผยแพร่เมื่อ 16 มิ.ย. 2024
- LDA is surprisingly simple and anyone can understand it. Here I avoid the complex linear algebra and use illustrations to show you what it does so you will know when to use it and how to interpret the results. Sample code for R is at the StatQuest GitHub:
github.com/StatQuest/linear_d...
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0:00 Awesome song and introduction
0:59 Motivation for LDA
5:03 LDA Main Idea
5:29 LDA with 2 categories and 2 variables
7:07 How LDA creates new axes
10:03 LDA with 2 categories and 3 or more variables
10:57 LDA for 3 categories
13:39 Similarities between LDA and PCA
#statquest #LDA #ML
NOTE: The StatQuest LDA Study Guide is available! statquest.gumroad.com
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
woohoo
Can you please do something on canonical analysis ?
@@WIFI-nf4tg I'll keep that in mind.
Hi Josh,
Love your content. Has helped me to learn a lot & grow. You are doing an awesome work. Please continue to do so.
Wanted to support you but unfortunately your Paypal link seems to be dysfunctional. Please update it.
website shows "Error establishing a database connection" !
the funny thing is, so many materials from this channel are for those university students (like me) but he keeps treating us like kindergarten children. Haha feels like i'll never be growing up, by watching your videos sir! QUADRO BAAM SIR, THIS WORLD HAS BEEN GONE TOO SERIOUS, THANK YOU FOR BRINGING BACK THE JOY
Thank you very much! :)
I am a kindergarden kid in this subject : (
@@daisy-fb5jc same here; i need someone to explain it like im a little kid
Remember: Us Adults are just big children
Every time I heard the intro music. I know my assignment is due in 2 days.
LOL! :)
@@statquest Thank you very much!
hahahah I'm on the same boat right now
Good to know I'm not alone.
10,5 hours till my machine learning exam. Thank you so much, I feel way better prepared than if I would have watched all of my class material.
Just spent hours so confused, watching my lectures where the professor used only lin alg and not a single picture. Watched this video and understood it right away. Thank you so much for what you do!
Glad it helped!
Hey what is the intro track called? I couldn't find it on Spotify. . . :D
This was honestly helpful, i am an aspiring behavioral geneticist (Aspiring because I am still an undergraduate of biotechnology) with really disrupted fundamentals of math especially statics. Your existence as a youtube channel is a treasure discovery to me !
Thanks! :)
This is amazing! 15 mins video does way better than my lecturer in an 2 hours class
While these 15 min videos are excellent for gaining intuition, you still often need those 2-hour classes to get familiar with the mathematical rigor.
@@elise3455 no you dont. math follows super quick and easy when you understood what it is about
@@NoahElRhandour yeah brother
@@elise3455 No you don't. Math become super easy once you understand what you doing
I really like the systematic way you approach each topic and anticipate all the questions a student might have.
Great video! I initially couldn't understand LDA looking at the math equations elsewhere, but when I came across this video, I was able to understand LDA very well. Thanks for the effort.
All of your StatQuest videos are awesome! Thanks for using your time to help others! Much appreciated!
You, sir, you are a life saver. Now in every complicated machine learning topics I look for your explanation, or at least wonder how you would have approached this. Thank you, really.
Awesome! Thank you! :)
Another excellent video just as great as the one on PCA. I read a Professor's view on most of the models and algorithms stuff in ML where he recommended understanding the concepts well so that we know where to apply and not worry too much about the actual computation at that stage. The thing that is great in your videos is that you explain the concept very well.
Thank you very much! :)
I really can't thank you enough for that...you did in 16 mins what I couldn't do in 4 hours. keep on the good work!! and thank you again !!!
Thanks!
I am so glad this channel has grown to around 316k subscribers. Very well explained. The best of bests.
Wow, thank you!
You are my hero. I am a senior hoping to get into data science and your videos are great and very helpful. Keep up the good work.
The song at the beginning made my day, even though I took wrong tutorial of Linear discriminant analysis in data science. Just awesome. Love it a lot. We need more and more funny teachers like you.
Thanks!
wow... my professor has been trying to teach me the concepts for weeks. and now I finally understand. Thank you so much. I will refer this to my mates.
Never thought anyone could explain things this easily. I appreciate the effort. Thank You
Thank you! :)
Hi Josh, Helpful to understand the differences between PCA and LDA and how LDA actually works internally. You're indeed making life easier with visual demonstrations for students like me :) God bless and Thank you!
Glad it was helpful!
Great video Joshua ! Looking forward to learning more from you !
Cheers from Japan !
Thanks for the video! I have an exam next week and even though its open book, I still didn't feel comfortable going into it. This video definitely helped!
Good luck and let me know how it goes. :)
This explains the beauty of LDA so well! Thank you so much!
Awesome! Thank you very much! :)
Brilliant video! Very helpful. Thank you.
always excited when i look for a topic and its available on statquest
Awesome! :)
You are awesome.Eventually,I was able to reach understanding point of machine learning staffs thanks to you.
Awesome! :)
Awesome! Even I get it and love it! I'm going to share one of your stat-quest posts as an example of why simple explanations in everyday language is far superior to using academic jargon in complex ways to argue a point. Also, it's a great example of how to develop an argument. You've created something here that's useful beyond statistics! Three cheers for the liberal arts education!!!! Three cheers for Stat-Quest!!
Are you somehow related to Joshua? :-P
@@rachelstarmer5073 ha
This helped me understand LDA before my midterm! I could not wrap my head around how the functions worked and what they did, but I got an "ah-hah!" moment at 6:49 and I totally understand it now. Thank you for explaining this!
Hooray! Good luck with your midterm. :)
I am able to grasp on this topic without being scared. Kudos to this channel
Thank you!
Loved the explanation. Your channel has been a truly invaluable source for studying ML. I was wondering whether you could make a video on the differences/similarities along with use cases for KNN/LDA/PCA.
I'll keep that in mind.
I really liked how you compared the processes of PCA and LDA analysis. I got to know a different way to view LDA due to this video
Bam!
Amazing. Thank you for this excellent video. Explained everything super clearly to me in a super concise manner without all the academic jargon getting in the way.
Glad it was helpful!
Again thank you! Your tutorials are super easy to understand!
Thanks as every time! The best explanations of complicated things!
Thanks!
woww........toooo goodddddddddddd.....dear Starmer...nothing to say..you are incredible...I am eagerly waiting for your next video...
This is an AWESOME vid. Thanks for making this idea so simple
This channel deserves millions of subscribers !!!!
Thank you!
Absolutely brilliant. Kudo's to you for making seem it so simple. Thanks!
subscribed just because the way you described this topic is so simple and understandable. nice job!
Thank you very much! :)
the song in the introduction is always awesome. thanks lol! and very useful video
Thanks!
much better than my university lecture that I listened to twice but couldn't understand ... this was awesome, thanks!
Hooray! I'm glad the video was helpful. :)
superb!! you have explained this concept amazingly well!!
Great video! You saved a struggling stats major for sure!
Thank you for the great explanation, Joshua! Great job!
I love your stuff, you have the knack to explain things better than most!
Thank you!
@@statquest Thank you so much for this video. I tried to understand LDA by reading lots of materials (books, papers, etc.), but none of them can explain things as clear as you do. Really appreciate it!
@@meng-laiyin2198 Thanks! :)
All your videos are extremely helpful!
very clearly explained. the video is very enjoyable to watch too! Statquest has all that is needed to learn machine learning algos and stats well
Thank you!
great video, you make all the academic terms very understandable, cheers from China
Thank you so much for helping me provide a faster solution for the confusion that has taken control of my head for 72h.
Happy to help!
thank you for your kind, slow, and detailed explanation😭
You’re welcome 😊!
I think I love you! thanks for these amazing videos! It's helping me to understand a lot of things for my PhD!
Simply superb! Awesome Josh!!!!
Thank you very much! :)
Thank you very much for the video, very intuitive and very logical. In the video you mentioned separation s, is this standard deviation within each group, or range(max-min)? Thanks!
Awesome! It'll be good to give some differences of PCA and LDA. For example, PCA is studying the X. LDA is studying the X->Y.
This channel is pure gold!
Thank you! :)
Really great videos, saved me from my data science classes. I'm applying for graduate program at UNC, hope I can have the opportunity to meet the content creators sometime in the future.
Best of luck!
I just watched all your videos for intro track :P ......awesome tracks and nicely explained videos
Awesome! :)
Wow , that is one of the best explanations of LDA
it helped me get an intuitive idea about LDA and what it actually does in classification
Thank You!
Hooray! Thank you! :)
Can you make a video on quadratic discriminant Analysis
@@Sachin-vr4ms Which part? Can you specify minutes and seconds in the video?
@@Sachin-vr4ms I'm sorry that it is confusing, but let me try to explain: At 9:46, imagine rotating the black line a bunch of times, a few degrees at a time, and using the equation shown at 8:55 to calculate a value at each step. The rotation that gives us the largest value (i.e. there is a relatively large distance between the means and a relatively small amount of scatter in both clusters) is the rotation that we select. If we have 3 categories, then we rotate an "x/y-axis" a bunch of times, a few degrees each time, and calculate the distances from the means to the central point and the scatter for each category and then calculate the ratio of the squared means and the scatter. Again, the rotation with the largest value is the one that we will use. Does that help?
@@Sachin-vr4ms I'm glad it was helpful, and I'll try to include more "how to do this in R and python" videos.
Hi, Joshua. Thank you for your videos, it’s really helpful. I have a question: so when you have a LDA to categorize n categories, does it mean that you need (n-1) axis to separate the points? In that case, how can I visualize them?
Wow!
At first "wt.f is Statquest"
then
At the end of video, STATQUEST! and I checked on the description. Its a great website !
Thanks
Amazing! I subscribed after watching your video only twice!
Wow, thanks!
Once again, a fantastic job. Thanks, StatQuest.
Thanks again!
It was a very helpful video. I get to understand it in the first attempt only. Thanks a lot for this video sir.
Hooray!!! I'm glad the video was so helpful! :)
Too much time and effort spent, but they worth it. Best explanation I watched after six weeks of search. Cordially thank you.
Thanks! :)
Josh. you are an amazing teacher. i have learned so much from you , a big thank you from the bottom ofmy heart. god bless you
My pleasure!
i recommended all your videos to my fellow students in the data analysis course
Thank you very much! :)
Thanks for this brilliant video! One thing I think is worth mentioning or emphasizing is LDA is supervised and PCA is unsupervised.
Noted
thanks for your videos dude, you make it way more simple to understand
Thanks! :)
I just graduated from high school, but your videos helped me understand many research papers. Thank you very much!!!!!
BAM and congratulations!!! :)
@@statquest DOUBLE BAM !!
Thankyou , Explanation of LDA & PCA is very clear....
Very nice video .
Admiring your passion and contribution !
Thank you! :)
Another clearly explained video by StatQuest!
BAM! :)
Very useful and intuitive, also sick intro music right there as usual! xD
I think this might be my favorite intro.
That helped me a lot! Thank you sooo much! Now I'm ready for my exam tomorrow :)
Best of luck!
This is a very good video! Thanks for your brilliant work!
That musical intro is much fun :D
Videos are awesome. Please post a video on eQTL and GWAS
Best explanation iv ever seen on ML. This is the first time iv watch ML youtube video without rewind :| ..
Keep Up bro..
Wow, thanks!
Very well explained, good job. Thx
So helpful!!! Super grateful I found you
Thanks!
Very illustrative, thanks for the video!
Thanks!
Thanks for such a crisp video mate :)
You're welcome! :)
Excellent! You are a better teacher than many overrated professors out there :)
Thank you! :)
Thank you for the illustration, it's very clear!
Glad it was helpful!
Thanks really helpful and i love your intro !
Thanks!! :)
Very nicely explained! Thank you very much Josh!
Hooray!!! I'm glad you like the video. :)
Tripple bam!!😁🤷
I didn't understand what the professor talked about in the lecture until I watched your videos. Thanks Josh, you save me!
Happy to help!
Clear and wonderful explanation. Thank you very much :)
Thanks! :)
Excellent video and video series, Josh! This has been an excellent learning experience while staying at home during the coronavirus pandemic. I was wondering if this type of LDA is also called partial least squares discriminant analysis (PLSDA)? Stay healthy and safe
They are different. PLSDA is more similar to PCA (I believe).
your videos helped me a lot. Thank you so much for such a clear explanation.
Thanks!
This lesson is just so beautiful dude!!!!
Thank you!
Fabulous work. Very helpful. Keep it up.
Thank you! :)
I know in any dimension this is an NP complete problem because it has the same cardinality of solution space as the subset sum problem: which is 2^N where N= number of data points, therefore 2^N is the number of all subsets one must check: i.e. project all possible subsets down to the new axis. Of course, once on a single ordered 1D axis, since the data points will now have a fixed order relative to each other, one has at most N places to make the separation.
Very nice explanation. The only issue I have is that the first and second axes for both PCA and LDA are not Gene 1 and Gene 2. They are instead some linear combination of Gene 1 and Gene 2. So in a 10,000 gene space, you will get some combination of some of the 10,000 genes that clearly separate the two groups. For example, LD1 could be one third of Gene 12 plus one third of gene 45 plus one sixth of gene 456 plus one sixth of gene 1,234.
Great channel... i love spending time here
Can i say that the most brilliant thing about statquest is the silly song??? love it, super fan
Thank you! Very intuitively explained!
Really great explanation. Thanks.
I get it! You sir is the best lecturer in statistics
Thanks!
Wonderful video, Great explanation!!! Could you please do a video on multi dimensional scaling and expectation maximization algorithm. Thank you
Thank you, very educative and entertaining!
You're welcome! :)