As a senior data science student, I want to enter the job market with as much knowledge as possible. Easy-to-follow videos like this make that goal so much easier. Thank you!
Sublime. This topic just came up in a data analytics course I'm taking (it wasn't a central theme of the lesson, but I hate not knowing the details sometimes) and this programme is a perfect complement to that. Like others have said, your style is intuitive but not over simplified. In general, I feel like you're striking a great balance between ease of understanding and mathematical rigour.
Very clear flow of explanation, thank you. I'm thinking that it would be useful to design a hypothesis test for the chosen setup to back up the idea of the final density and so to get an extra information along with the vertical position of the chosen point as of how much proof we have for the final result that is allowed by the number and positions of the known fixed points. More research would be nice.
Clear explanation and easy to follow, thank you! Silly observation: "Integrate over all possible weights of fish. All the way from negative infinity to positive infinity": I'm no ichthyologist or fisherman but I feel negative weight fish ain't an option.
Great explanation! Gaussian KDE is great for bimodal and skewed distributions. One downside with gaussian KDE is difficulty accurately modeling distributions with high excess kurtosis.
Thank you for this amazing video! But I have a question. At the beginning, the question was defined as "What is Population Density". But, does not KDE give us the density of a spesific data point instead of the whole population as estimated? Because the result is found as using a data point which does not appear in the results. Therefore, we actually try to understand the density of a spesific point instead of population. Do I get it wrong or was the question generalized?
Would it be fair to say that this method is applicable mostly when the amount of data is relatively low? With large amount of data you'd just plot a histogram and be done? What sort of data do you visualise with KDE?
Thanks for the good explanation about KDE method. could you please make a video about prediction intervals PI that sometimes uses the KDE method? thanks!
You integrate cause you're working with continuous functions. It is already normalized since the squared difference could be at most 1. We also want a good estimsted distribution to perform well on other samples from the true distribution. That's why we take the expected error on various samples
Great content, and very clearly explained. May I just suggest starting from "white sheet" or almost? it doesn't need to be written or drawn incredibly well but the full sheets feel pretty overwhelming
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As a senior data science student, I want to enter the job market with as much knowledge as possible. Easy-to-follow videos like this make that goal so much easier. Thank you!
Great to hear!
"senior data science student," okay undergrad
Enthralling video on this topic. I cannot thank you more for the lucid explanation on this interacted topic.
Sublime. This topic just came up in a data analytics course I'm taking (it wasn't a central theme of the lesson, but I hate not knowing the details sometimes) and this programme is a perfect complement to that. Like others have said, your style is intuitive but not over simplified. In general, I feel like you're striking a great balance between ease of understanding and mathematical rigour.
Love the content. Easy to follow and understand. You are one of the best teachers in the data science field!
Amazing video, so clear and concise. I learn better with visual and conceptual ideas first before diving into the maths. Thank you!
Glad it was helpful!
Such a great lesson! Lately I've been very frustrated with the unintuitive and bloated language of my university lectures and texts. Thank you!
10 times better than any materials i had from uni, and now i actually get it!!
Crystal clear! Appreciate your effort for making such amazing videos!
Thank you for this video. Way way better teaching than what I am getting in university
Amazing video! Clearly explained with an easy to understand example. Thank you
thanks!
this man is a magician!
Quality content here. Also examples are nice and clear!
Love it, amazing work in this video, congratS!
Thanks a lot!
Very good and intuitive explanation
Thanks!
Very clear flow of explanation, thank you. I'm thinking that it would be useful to design a hypothesis test for the chosen setup to back up the idea of the final density and so to get an extra information along with the vertical position of the chosen point as of how much proof we have for the final result that is allowed by the number and positions of the known fixed points. More research would be nice.
Just one silly question pl. Which tool did you use to plot the graphs at 15:20 ?
Clear explanation and easy to follow, thank you! Silly observation: "Integrate over all possible weights of fish. All the way from negative infinity to positive infinity": I'm no ichthyologist or fisherman but I feel negative weight fish ain't an option.
Negative infinity to positive infinity just means that you've to integrate the PDF over its domain :)
the only video i undestood without mathematical jargon.
Big fan of the fish drawings :)
Great explanation! Gaussian KDE is great for bimodal and skewed distributions. One downside with gaussian KDE is difficulty accurately modeling distributions with high excess kurtosis.
Banger video as usual
Excellent video, subscribed!
Awesome, thank you!
Really nice explanation. Thanks a lot.
thank you very much, it has become so clear.
so useful. Thank you so much man
Always the best!!
Thank you, Great Video:)
No Problem !
Glad you liked it!
Thank you for this amazing video! But I have a question. At the beginning, the question was defined as "What is Population Density". But, does not KDE give us the density of a spesific data point instead of the whole population as estimated? Because the result is found as using a data point which does not appear in the results. Therefore, we actually try to understand the density of a spesific point instead of population. Do I get it wrong or was the question generalized?
Would it be fair to say that this method is applicable mostly when the amount of data is relatively low? With large amount of data you'd just plot a histogram and be done? What sort of data do you visualise with KDE?
Thanks for the good explanation about KDE method. could you please make a video about prediction intervals PI that sometimes uses the KDE method?
thanks!
You say that Kh is normal centered at Xi, but the way you've set it up, it looks like Kh will be centered at 0 (i.e., when x = xi, density is max).
Very nice!
You are best❤
Amazing video thanks!!!!
great video but i am confused about why we didn't use just 1/n*(sigma(...)) for MISE formula but integral and expected value.
You integrate cause you're working with continuous functions. It is already normalized since the squared difference could be at most 1. We also want a good estimsted distribution to perform well on other samples from the true distribution. That's why we take the expected error on various samples
Part of non parametric regression for postgraduate statistics
Great video
Thanks!
loved the way teach
Thanks!
Wow you are great can you make full Videos about ml using book An Introduction to Statistical Learning
- with Applications in R?
Thanks! I’ll look into it
@@ritvikmath please doit you explain things Easy and simple, given the must information of things so it's very Easy for us to remember
Great content, and very clearly explained. May I just suggest starting from "white sheet" or almost? it doesn't need to be written or drawn incredibly well but the full sheets feel pretty overwhelming
thank you
very very good one pound fish
Come on ladies, come on ladies
So helpful, better than my professor lol
Thanks!
It would have been nice, if you had written down the math at 11:50.
Was expecting the terms to be “over fitted” and “under fitted” but they turned out to be “under smoothed” and “over smoothed”. So disappointed.