Відео

Crisp and clear. Thanks for explaining.

I didn't get how you considered t=4 for outlook

Video of useful but I doubt interpretation of variance explained by model. shouldn't we be looking at proportion and cumulative columns for variance explained rather than looking at ranked attributes figure?

Best video ever the best I say

Is this new moon app. Safe from third parties apps? Is it safe to leave my card no.? Also, I just wanted to try for one month first.

Vtu guys give a like 👍

anyone from scc 202?

Great explanation, Thank you

Hi, regarding feature extraction using PCA I applied PCA filter on input dataset that consists of 13 predictors and got 18 principal components. Is it possible? At first, the input dataset consists of 6 missing values with that it produced 12 PCs when replace the missing values with majority mark of that particular features I got 18 PCs. May I know what exactly the concept behind this? By the way I used Cleveland heart dataset and WEKA platform. Thank you.

My ranked attribute values aren't adding upto 100.. if anyone could explain what am I doing wrong?

Awesome. Thank you!

Poor audio

isn't, for example, distance 2 must be distance 2 - distance 1?

Thanks, my lecturer spent 15 minutes explaining this through a long winded example about a boy and a girl going to lunch and the correlation relating to whether the boy was in love with the girl and was purposely trying to see her at lunch. Thankyou for not wasting my time like my $2000 uni course.....

Great job!

Hi Chris great video can I ask a question please

Hi

one question, are these just some random questions that associated with the task or it's kinda standard...

thank you very much for explaining the concept with a very easy and understandable example!

Great explanation sir. but what after that ?? how to use this data in my model. I'm making a IDS model for detecting attacks and I need to reduce the features to get faster and accurate detection

комментарий

You are the best explainer in this topic i've found in UA-cam. The indian guys i've found are really amazing, but the bad english and the lack of focus are barriers to acknowledgement. Congratulations and thank you for have a script before recording the video and for being so patient during it.

Great video, clear to understand...

hey thanks a lot, the tutorial really helpful !!

Great content. Thank you so much for explaining it. But I think it's worth pointing out that the ranked values don't add up to 1. When you try with other datasets, these values are all less than 1, but certainly aren't adding up to 1. For example, I got 0.74, 0.52, 0.39 for the first 3 attributes (there were more) for the diabetes dataset. I would say that it was just a coincidence the values got in the video, but even in the cpu dataset that was used in the video, they don't add to 1 as well. They appear to, at a first look, but when you actually add them up, you get 1.0473. Close, but not 1. Thanks anyway, all the rest is really helpful.

Hey. Is there somewhere i can find this test? :)

Was there a reason play button was not replaced to stop button while recording?

This is such a great explanation. Thank you

Can you explain did you captured movement time in the data?

congnintgoivhfsdgb walktwefuiah

First of all thanks for the great video! It was very helpfull in understanding a Cognitive Walkthrough. But one Question :P Obv the camera symbol is too close to the stop button, but isn't that the reason why they actually use a circular symbol instead of the camera symbol for recording (since it's also closest to the common analog button)? And aswell don't u think an "X" will be missleading for canceling the action rather than just stoping the video?

in rules building, you have two things cause and result. you can guess leaf is a result because it carries a decision. for example, your diabetes data starts split with the age attribute and after two splits using different attributes get a pure subset. Thus If Age >=30, Fat person, and No regular exercising, then Yes patient has diabetes.

Awesome job with this tutorial, very helpful thanks

not my proudest fap

Thank you for this excellent video. If you are working with R you can use the OneR package for that, it is on CRAN. Please watch this video for a step-by-step introduction: ua-cam.com/video/AGC0oRlXxgU/v-deo.html You can find out more about it here: cran.r-project.org/web/packages/OneR/vignettes/OneR.html

Thank you for this excellent video. If you are working with R you can use the OneR package for that, it is on CRAN. Please watch this video for a step-by-step introduction: ua-cam.com/video/AGC0oRlXxgU/v-deo.html You can find out more about it here: cran.r-project.org/web/packages/OneR/vignettes/OneR.html

nice, mate..superb explanation...

thanks for sharing, very good tutorial!

Hello, quick question. Should the data be normalized before I use PCA? Thanks for sharing.

what type of pen is that? I had one of them in highschool and it was my favorite pen of all time.

Thanks, very intuitive...

Hello. Really practical video. I have one question: How do you define the threshold? I mean why 50% or 40%? Do these numbers come out of random? During the first phase, I realized that it is easy to select features since they are ranked ( so they use something like entropy algorithm or so). But what about these percentages that you are mentioning? Where do they come from? Thank you in advance!

Great . Amazing Job

I watched the whole video & I swear I still don't know what it is ! :/

Very well done. Great reference for students and those new to UX.

Very Simplified , Great stuff

Rainy should be 3/9

Thank you so much! its to ease to understand it ! keep it up #usabilty #engineering

For some reason my ranked attribute variance doesn't add up to. Does anyone know whats the isssue with it

Can I use PCA with dataset containing non-numeric attributes?