StatQuest: PCA main ideas in only 5 minutes!!!

The main ideas behind PCA are actually super simple and that means it's easy to interpret a PCA plot: Samples that are correlated will cluster together apart from samples that are not correlated with them. In this video, I walk through the ideas so that you will have an intuitive sense of how PCA plots are draw. If you'd like more details, check out my full length PCA video here: • Principal Component An...
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0:00 Awesome song and introduction
0:27 Motivation for using PCA
1:23 Correlations among samples
3:36 PCA converts correlations into a 2-D graph
4:26 Interpreting PCA plots
5:08 Other options for dimension reduction
#statquest #PCA #ML

[@statquest]

Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/ Special thanks to PROTIST for the Russian subtitles!!! :)

[@falaksingla6242]

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.

[@tuongminhquoc]

I appreciated your effort spent on these videos. Sadly, since I am still a student, I have no money to support you just a bit. So, I have spent much of my effort to translate your videos into my language as it is my best language as a thank to you. Hope you would accept my thank.

[@statquest]

Thank you very much!!!! :)

[@tuongminhquoc]

@StatQuest with Josh Starmer I didn't think you would check this that soon :))) thanks for accepting my contribution!. I'll translate more of your videos whenever I have free time (and wifi haha :D )

[@statquest]

@@tuongminhquoc I really appreciate it! :)

[@leonguyen1204]

No wonder the subtitle was spot on! Great work mate, thanks for that! Also thanks @StatQuest with Josh Starmer, nice video with simple explanation, I'm trying to make sense of it.

[@ngoctandang9307]

Really excited when watching this video in Vietnamese subtitle, thank you!!!

[@Lastmomenttuitions]

Very nicely explained thank you so much for Putting this

[@statquest]

Hooray! I'm glad you like it. :)

[@rafaelerwin]

Without Statquest, I cannot imagine how hard my academic and professional life would be.... Thanks a lot Prof Starmer!

[@statquest]

Thanks!

[@fartissimo]

I enjoy your videos and you are performing a valuable service. The few things I would mention that would be helpful are that PCA is really a measure of covariance in a sample and that PCA does NOT provide ANY indication of statistical significance. Understanding Covariance is helpful to really understand PCA. Also, PCA is particularly useful when patterns emerge between experimental and non-experimental parameters. If patterns associated with experimental parameters are observed (i.e. treatment conditions) it indicates that there may be changes between samples/populations that are of interest; in cases where there are patterns associated with non-experimental parameters (such as collection date or incubation conditions) it indicates that the date of collection resulted in more variance than experimental parameters. In such a case, it points to a possible flaw in experimental design so that it would be of benefit to re-evaluate sample collection/preparation/incubation etc... in the workflows to minimize the influence on the studied populations.

[@guandongkou178]

Thank you Josh for the clearly explained abstract concepts! It is even more informational than a 2-hour lecture in a college.

[@statquest]

Glad it was helpful!

[@aborucu]

This is by far the best on internet, Khan Academy doesnt have this content, all courses on coursera,udemy either wave formulas in the air or dont bother for a simple yet enlightening explanation..This is what practicioners need.Bravo!

[@statquest]

Thank you! :)

[@chintanchawda9725]

Every maths prof. must be like, way of explaination is as simple as possible. Thank you.

[@MeetPatel-jx6sn]

If my university would have been teaching 10% like you I would have completed my engineering in just 1year Awesome video ♥

[@statquest]

Bam! :)

[@rorals4814]

Your voice tone reflects how confident and smart you are... Thanks, plz we need more videos related to machine learning stuff

[@HoriBar993]

You have a teaching talent. Thank you for the all your videos!

[@alondracortezenciso4272]

You just saved my life, sir! Doing journal club tomorrow and I had no idea how to read a PCA from steady-state metabolomics. Thank you!

[@statquest]

Glad I could help!

[@sidekickwatson]

Thank you for these sequencing, singing, and recipe videos, this channel needs more subscribers.

[@statquest]

Thank you very much! :)

[@cynthiacabales22]

You are helping me survive my Research Analytics class - HOORAY! :-)

[@statquest]

BAM! :)

[@chriscollins5320]

What a great video that clearly and concisely explains PCA. Great job, keep these up.

[@kesialeite7913]

Thanks! Now I finally understand what I am doing in the lab! 🇧🇷

[@statquest]

Hooray! :)

[@ohromujici]

Thanks! This was a great overview. I am in big data for a pharma company and we added PCA to one of our data tools. The documentation we received was a little "dry" so thank you for putting this into easy to understand key concepts. This helped a lot. Also, I did my original graduate work in mRNA decay so bonus points for dragging mRNA into this. :) :) :)

[@KingstonUponThames]

Best explanations of PCA in layman terms. Great work. Thank you!

[@statquest]

Wow, thanks!

[@nourajamal8166]

This is one of the most great channels I have ever seen . If u are looking for a good ,easy and quick explanation you are in the right place ;)

[@statquest]

Wow, thanks!

[@jessefp1940]

Extremely helpful thanks, explaining the principal components in the order that you did, you nearly lost me I would consider rearranging the explanation of what pc1 and pc2 are in the video.

[@statquest]

Glad you liked this video! If you have time, you should check out the new and improved version (which is longer, but it's worth it, I promise you): kzbin.info/www/bejne/fJjEnI2ta7Bkh7M

[@ZakLiTLearnfortheEnvironment]

I wanted to browse a video with the title ''HOW TO THANK STAT QUEST?" the only answer I got is just pray for the channel's success....

[@statquest]

Thank you! :)

[@quililank]

I've been searching PCA for dummies for so long and I'm glad I found this! I can finally understand what the researchers in this journal I'm reading are trying to say Haha!

[@statquest]

Hooray!!! I'm so glad I could help. If you want to go a little bit deeper, let me recommend my other PCA video. If you watch that one, you will be a PCA master! kzbin.info/www/bejne/fJjEnI2ta7Bkh7M

[@naturaldisastertv3298]

i was also having the same problem. i watched his 20 min video but I couldn't understand anything.

[@digigoliath]

Wow, this sheds a lot of light on dimension reduction. Very clearly explained & illustrated. TQVM!!!

[@statquest]

Thanks! :)

[@eemmmaaadd]

I always come across your videos when looking for stat information. And always your videos are the best.

[@statquest]

Awesome! :) Thank you! :)

[@adelutzaification]

Great video! I became a bit addicted to the StatQuest videos and my anxiety levels increased for a while, not seeing the usual morning Monday upload. Now I need to figure out what t-SNE plots are... ;)

[@luonhanquocvu8411]

As I learn PCA in a machine learning course, I knew that you have a good video explanation on this topic!! thanks!

[@statquest]

Glad it was helpful!

[@verdasney]

It is more understandable that my 1.5 h lecture and a good start of PCA class. Thank you for the video. Very well created.

[@statquest]

Glad it was helpful!

[@Nicblocks]

I had to wrap my head around PCA plots as part of a presentation and just could not understand it. This was really well done and I'll be taking this knowledge in with me. Thank you!

[@statquest]

Hooray! :)

[@boundlessbrittany]

Holy moly. I finally understand the concept of PCA plots :O THANK YOU SO MUCH

[@statquest]

Bam! :)

[@findingnemo4637]

100s of lines in 5 min.. great work sir.

[@zes7215]

wrg

[@marjanyaghoubi5729]

Hi sir, your explanation is very clear and vivid, I truly appreciate for it. Please do a video on Laplacian matrix and its application in dimension reduction.

[@timlofton6595]

Good stuff Josh. Going to the lengthier version to further blast this through my thick skull. 😃 Appreciate your efforts with this!

[@statquest]

Enjoy!

[@robb8763]

so well structured, so on point - like all of the videos. very rare quality of a teacher: the comibnation of deep understanding AND the ability to narrow it down... almost like reducing dimensions to make things simpler to understand ;) what a great work!

[@statquest]

Thank you! :)

[@liranzaidman1610]

2:03 People should stop here and listen very carefully because this is a really important concept, and I mean - Really important! When analyzing data and the parameters effecting the outcome of something - this must be the way to think. Great work

[@statquest]

Nice! :)

[@wesleysbr]

Best regards from brazil, you are the best! thank you

[@juanno3581]

Thanks for the explanation!! It makes sense to use it with dendrograms for plant breeding!!

[@statquest]

BAM! :)

[@putriestimandasari4173]

I looovee your voice and your explanation... Great job, Sir.. Thank you !!!

[@statquest]

Hooray! I’m glad you like the video!! :)

[@nileshhalder7058]

I just cried after watching your video.. I looking to easy concept for one hour .... Thank you

[@statquest]

Glad it helped!

[@edupazz]

Great!! Your Calm and crystal pronunciation makes the concept very clear to understand. Thanks

[@statquest]

Thank you!

[@jorgepinto2241]

Excelent work! I love your video, It is so well explained. Thanks a lot!

[@amitrokade1140]

Finally understood it! Thank You for a great video

[@thatbackbenchdude]

StatQuest is the best

[@statquest]

Hooray!!! :)

[@nadhirahbaharin.]

you need more subscribers!!! Thank you so much your videos are life saver

[@nadhirahbaharin.]

I didn't skip the ad to support you (it's the least I can do haha. )

[@andersonbessa9044]

Great explanation as always! Thanks a lot for your effort!

[@statquest]

Glad you liked it!

[@UKFishCam]

Thank you so much for making this video! I've got my final year project due soon and Id lost the plot before this video!

[@statquest]

bam!

[@naturaldisastertv3298]

i watched your 20 min video too. But this was easier to understand. Thank you so much.

[@statquest]

Hooray!

[@winniewu8581]

Thanks again. Good as always. Thanks for the weight and height example!

[@statquest]

Thank you! :)

[@AbHiShEkdAS1505]

I have taken ML this semester and to be very honest I am understanding all the concepts from your videos. I would be really grateful if you could upload a playlist on Neural network and Deep Learning.

[@statquest]

Awesome! Neural Networks should come out in the next few months.

[@LorwaiTan]

@@statquest Thank you for so clearly explaining these concepts. Looking forward to your Neural Networks videos! Will share your videos with my colleagues.

[@mc9272]

StatQuest is really the best! that you so much to prevent my brain to explode!!!

[@statquest]

Thanks!

[@edsonwinnerify]

Thanks Josh, I can always get something new from your videos.

[@statquest]

bam! :)

[@noneoftheabove1589]

I love your humor! What a lovely way to present and explain. ahem.. what could be daunting to some lol (such as myself!) Grateful for the work and the passion! Keep up the good work! A new subscriber!

[@statquest]

Thanks so much!

[@ghatshilagogol]

awesome pca for dim reduction with vertical+horizontal+depth all in one 3-d rotates

[@statquest]

:)

[@menghongpor2667]

Thank you for all the videos. It is super easy to understand.

[@statquest]

Thanks! :)

[@96unicorns]

This was such a great explanation and so entertaining!

[@statquest]

Glad you enjoyed it!

[@jeremmoses8562]

You tech Harvard type of kind of stuff in elementary school way in all your videos, how do you that man! It's amazing, Thank you so much

[@statquest]

Thanks!

[@newlifestartsnow4444]

Thanks for the video. What do the sizes and colours of the circles represent in the 3D scatter plot which appears around 2'47"? Are these just an aid for giving the plot depth?

[@fredkiffer6753]

Thank you for existing!

[@statquest]

Thanks!

[@giraldeaufagaerone4213]

thanks for these videos it helps me understand better compared to classes

[@statquest]

Thanks! :)

[@riebeckita6798]

StatQuest is the best! Do you have any suggestions on how to do the last step: take clusters identified visually by PCA and clearly separate them? Particularly when they're not as cleanly clustered so discrimination becomes more subjective!

[@statquest]

It's a good question. You could try k-means clustering.

[@gabrielbolivar16]

Hi! Your videos have helped me to understand multivariate analysis! Do you have some video about PCR or PLS? I would really appreciate them. Have a good day!

[@fereidoon82]

well done, short and clean, thank u

[@eveqian]

really good video!! thanks for the super comprehensive explanation! (I'm also a University of North Carolina graduate. Tar Heels!)

[@statquest]

Go Heels!

[@MrKB_SSJ2]

This was so smoothly explained. Thank you soooooooooooooooo much!!!!!

[@statquest]

Thanks!

[@MeditaBrasil]

So well explained it!!! AMAZING!!! Thank you very much for making this video!!!

[@statquest]

Glad you enjoyed it!

[@ameliadiazdepascual9127]

Congratulation. it is an excellent example of PCA.

[@statquest]

Thanks! :)

[@iftrejom]

The way you easily and calmly explain such complex topics is outstanding. Thank you very much.

[@statquest]

Thanks!

[@sanathgunawardena832]

You're a lifesaver, Josh! Thanks!

[@statquest]

Thanks!

[@ArtsIntentive]

WOW. YOUR EXPLANATIONS, MY GOOD MAN, WERE CLEAR.

[@statquest]

Thank you! :)

[@nftk5413]

i love your way of explaining things tnx alot ...these videos are really helpful

[@statquest]

Glad you like them!

[@rezaheydari3661]

thanks Josh your videos are amazing!!!

[@statquest]

Hooray! If you have time, check out the new PCA video that I made. It's longer, but it goes way deeper and it's just as easy to understand: kzbin.info/www/bejne/fJjEnI2ta7Bkh7M

[@Muuip]

Great concise presentation! Much appreciated!👍

[@statquest]

Thanks!

[@Diegoblismartmedpengar]

oh man!! thank you, I needed this so bad

[@statquest]

:)

[@DrATIF-ij9hy]

Thank you for the Arabic subtitling, as I have always recommended your channel to my students; best wishes.

[@statquest]

Thanks!

[@MM-qq6xp]

Excellent demonstration

[@user-pw7hc6fd3h]

Such a beautiful work Great video indeed thanks !!

[@statquest]

Thank you! :)

[@rajivdhya9625]

Great videos, In this one, I think we could have talked more about genes being the dimensions, ie. the gene1, gene2, gene3, gene4,.. of each of the cells. And with Pca we want to reduce the dimensions from say from x genes to y PCs.

[@rajivdhya9625]

Joshua Starmer Thanks, your videos are great work.

[@lakep7798]

Wow--this was SO very helpful, even if corny at times, lol. Thank you so much!

[@statquest]

Thanks so much! :)

[@SuperLameStuff]

Thank you for your clarity!

[@statquest]

Thanks!

[@xialinsang6403]

I really really enjoy your videos!!! Thank you so much !!!!

[@statquest]

Thank you! :)

[@majfbi]

Thanks for the priceless videos, Can you make a video on FPCA, please?

[@anapeleteirovigil3199]

Came for the explanations and definitely stayed for the openings

[@statquest]

bam!

[@vietnamesefisheatbananas8607]

Simply brilliant! Could you do a similar 5 minute video on factor analysis?

[@statquest]

I'll keep that in mind.

[@azzywazzy5317]

Excellent video, really excellent. You are great at what you do.

[@statquest]

Thank you!

[@laure189]

Fantastic video, thank you!

[@statquest]

Hooray! :)

[@lauranajeracortazar2610]

Thanks for your videos and work! Silly question: how do we assign the “variable” explained by PC1. So, we get the scores from The PC and they supposed to be close to 1 or -1 if they are better explaining each axis... right? By any chance you have a video explaining this? Thx!

[@statquest]

If you want to learn more about the variables that are important for each principal component, check out this follow up video: kzbin.info/www/bejne/fJjEnI2ta7Bkh7M

[@daphnes1001]

Thank you so much!

[@arkadipbasu828]

Clearly explained Josh. Thank you

[@statquest]

Thank you!

[@sridharmaloth87]

Thanks for keeping this video

[@not1AM]

thanks man.. your videos are both very informative and fun.. really appreciated ❤❤❤❤❤❤

[@statquest]

Glad you like them!

[@nehatevathia3350]

Can we have a video comparing MLE vs Gradient Descent vs OLS? When to use what and how one benefits over the other. Can we have MLE vs Gradient descent approach for Logistic or other models? Thank you :)

[@domenicodifraia7338]

Your're great man!

[@jerrytravels7261]

YES!! What an awesome video. Love everything about it. So clearly explained, I like that you're only saying things that are humorous or extremely relevant. +1 subscribers.

[@statquest]

Awesome!!! :)

[@QuynhMata]

Phew, thank you so much! This was very helpful.

[@statquest]

Glad it helped!

[@adelutzaification]

Thank you. Good to know.

[@dandanzhang2891]

Great job! like your lovely voice!

[@carlosalfonso5829]

Excelent video thank you very much!

[@statquest]

Hooray! I'm glad you like it. :)

[@nompumelelongubane7579]

simple enough for my understanding. thanks a lot.

[@statquest]

Glad it helped!

[@prabhpreetkaur2029]

Thank you for such a nice explanation

[@statquest]

Thanks! :)

[@jonathanuis]

How long do you take preparing such an amazing explanation?. Everything is so clear that I can not even imagine why professors at university are not able to explain this way, after so many years of experience. Everything is so mathematical and let us say not intuitive at all, that make us think we are dumb.

[@statquest]

Thank you very much! :)

[@tanaychowdhury1589]

These videos are Gold!!

[@statquest]

Thank you! :)