Пікірлер

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  @fosheimdet6 сағат бұрын

  Did you do training-test split on the data?

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  @astrophage3817 сағат бұрын

  I'm a simple man. When Ritvik posts, I watch.

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  @acdundore8 сағат бұрын

  Knocking it out of the park, as usual.

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  @shubh431914 сағат бұрын

  Great video . Can you do some Multivariate analysis data series with different business case scenarios

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  @somethingness21 сағат бұрын

  What an excellent teacher you are! Thank you for all your videos.

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  @mukeshkumaryadav350Күн бұрын

  Great Explanation

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  @yuxiang3147Күн бұрын

  How do you decide whether to accept a sample proposed by g(x)? For example, say f(s) / (M*g(s)) = 0.4, do you accept it or not ?

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  @reemaalamoudi4959Күн бұрын

  Amazing

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  @andreapanuccio295Күн бұрын

  I would add that it's not experimental but quasi experimental because it's not really granting that the effect is due to our treatment. It might be due to a common cause or be causally biased by it so we are also assuming causal sufficiency and a sufficient knowledge of the underlying process. Still we have no way to guarantee that some unobserved cause happened in that time window. Good and clear video, as usual btw, keep going bro °u°

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  @TechnologyBuddaКүн бұрын

  gender pay gap is fake

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  @michelarruda7634Күн бұрын

  Great content! Thanks for posting it

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  @ib2002-o6dКүн бұрын

  Stationarity conditions - mean = c - var = c - no seasonality or periodic repetiotions

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  @ib2002-o6dКүн бұрын

  ACF - takes into account all possible t-k which can affect t. Calculated using Pearson coefficient. Direct and Indirect effects. PACF - takes into account only t-k, and how it affects t. A regression line is fitted for this. The coefficients obtained explains the affect of t-k on t. Direct effects only.

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  @ronit62352 күн бұрын

  Dear ritvik why does the determinant value of the first explanation results comes out to be zero(I did try to change some conditions like a single path to every next point but then also that value also comes out to be zero ) what does it indicates?

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  @ib2002-o6d2 күн бұрын

  Notes for future - TS is an extrapolation problem and error keeps on increasing as we move away from known data - Reg is an interpolation problem and error is more or less same, since prediction is usually made in the range of available data.

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  @blairt81012 күн бұрын

  saved my life again!

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  @samuelrojas37662 күн бұрын

  I am still confused about how you developed the kernels in the first place. I know what they do but don't know how to obtain them without using the transformed space.

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  @aftermath__70602 күн бұрын

  hi R, one ques. in what sequence should we watch these lectures ? the playlist seems to be jumbled , or it serves a purpose ?

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  @robharwood35382 күн бұрын

  Hi Ritvik! The way you've set up the linear model(s), the intercept parameters B0 and B2 will represent the intercept from Tt=0, rather than at the particular time that the interruption happens. So, if you got a large positive change in the B3 slope parameter, you'll probably get a negative change in the B2 parameter (since a steeper line at positive Tt will intercept the yt axis *below* where the original line did! Wouldn't it make more sense to do a shift/translation of the Tt parameter so that it works as if the interruption happens at the yt axis? For example, use a translated variable Ut = (Tt - t). So, if the interruption happens at t = 75, then when Tt = 75, Ut = (Tt - t) = (75 - 75) = 0. And your model can then be: yt = B0 + B1⋅Ut + Dt⋅(B2 + B3⋅Ut) At least this way, the B0 and B2 params will have a much more interpretable meaning. B0 will be the value of yt at Tt = t of the main linear model, and B2 will be the *initial change* in vt at Tt = t. In other words, how much 'immediate effect' did the interruption have; how much of a vertical 'jump' up or down. Granted, to calculate vt at any particular Tt, you'll have to first convert to Ut, but that's not so bad, just a simple shift. And if you really need to find the linear params in terms of Tt, it's fairly easy to just plug in Ut = (Tt - t) and expand out to find the transformed linear params for Tt.

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  @davidheilbron2 күн бұрын

  Thank you so much

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  @wjrasmussen6663 күн бұрын

  How can we take advantage of the GPU?

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  @marcfruchtman94733 күн бұрын

  Thanks for this tutorial on ITS. I have to admit, I am a bit confused by this example. I wasn't sure at first why. But basically, it is because the "data" is "too simple". At first, I look at this and I say "Any one" can see that Pre-advertising is not as good as "post" advertising treatment. So, Why would I need to use this technique. Why not just count sales per "month" and say, yup ... we boosted total sales per month. There's nothing inherent in this data sample that confounds the analysis or makes it difficult or non-obvious. On the other hand, if you could show something where the sales data points are more difficult to track, so that it is non-obvious by immediately looking at the data (in graph form) that the trend after treatment is looking seemingly more positive, but using ITS we can see that while visually it seems to look better, in fact, it is not as good after treatment (per this method.) And perhaps a second example, where it seemingly looks more negative after treatment, but by using ITS we can prove that it is better after treatment Then, I think those would give more credence to this method. Because as far as I can tell, the graph makes it far to easy too see the trend... so why bother with ITS unless it can show something that isn't obvious. And, again, thanks for this explanation on Interrupted Time Series.

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  @vanshr.sachan2643 күн бұрын

  i still dont get the part of getting the effect without the experiment.... if i dont have the time series data of my ice cream shop where i decided to advertise as right now im in middle of taking this decision then how can i fit a model which can measure the effects of it?

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  @Petershd1383 күн бұрын

  I confess, I didn't understand any single word of explanations from my MIT professor about perceptron. How ever, after I saw this video and understood clearly what the idea is. Thanks.

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  @cornagojar3 күн бұрын

  thanks, may I ask what do you do for a living?

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  @polgonzalez9783 күн бұрын

  the equation of the hyperplane is w·x+b=0, isn't it? The video says - b=0 instead of +b=0

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  @jfndfiunskj52993 күн бұрын

  Great vid. Please explain how to compute the confidence intervals.

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  @liltarnation26413 күн бұрын

  Absolute legend

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  @MrEo894 күн бұрын

  Man your channel would blow up spectacularly if you invested the time into learning how to make really nice visuals.. the whole poorly hand drawn example thing is really 2005 && screams laziness and/or amateur..

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  @junal274 күн бұрын

  Excellent video and content, thank you. I am not a trader nor a financial person but the training window size vs. the holding window size may be part of the problem unless the choosen entry time would be an outlier by chance. The holding size may play a huge role in certain market dinamics with periods of revovery much greater than the chose holding window time. When market goes down correlations tend to one and in many cases do revert to positive from negative in calm situation. Best

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  @siddharthabhakta32614 күн бұрын

  I thought U is mxm, V is nxn and SIGMA is mxn

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  @TheKyprosGaming4 күн бұрын

  Can you show us the code for this, especially for VAR

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  @ritvikmath4 күн бұрын

  there's some pretty basic code here scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py

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  @andrelu35614 күн бұрын

  This means that apples are far better than bananas right? So when I've been told that you cant compare apples and bananas ... thats a lie ...

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  @meanreversion10834 күн бұрын

  Thank you for the video. it was nicely explained. There are a lot of simplifications. Could you also talk about how best select sigma and l - is it all done empirically? also do you have any example of implementation?

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  @hoatanky39264 күн бұрын

  Pls recommend a book for data science n machine learning

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  @jessicas29784 күн бұрын

  Very clear and helpful. Thank you so much!

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  @ritvikmath4 күн бұрын

  You're very welcome!

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  @TheRealHassan7894 күн бұрын

  So simple… but so powerful!

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  @ritvikmath4 күн бұрын

  glad you think so!

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  @thejll4 күн бұрын

  Go on, tell us how to test if and when a change occurred :)

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  @christusrex3344 күн бұрын

  Any data science text book recommendations?

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  @tuananhtran75323 күн бұрын

  Pattern Recognition and Machine Learning from Bishop

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  @rishikalodha12364 күн бұрын

  Thank you for this

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  @user-cy7up3kq7o4 күн бұрын

  I would argue that 2 has seasonality...

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  @GeoffryGifari4 күн бұрын

  Hmmm i noticed that if two categories are strongly correlated, the plot will look close to a straight line. Going to multidimensional space, that "line" looks like the vector u1 in the video, on which the data are projected. Does that mean PCA will perform better the more correlated two (or more) categories are?

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  @studentaccount43545 күн бұрын

  Can you do this again? Polls mean nothing. There are too many confounding variables.

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  @just_a_viewer55 күн бұрын

  amazingly taught. thank you so much

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  @zsomborveres-lakos5 күн бұрын

  nice

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  @michaelvogt77875 күн бұрын

  Nicely done.

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  @domillima6 күн бұрын

  Your platform is awesome man keep up the great work!

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  @domillima6 күн бұрын

  This is an amazing analysis/discussion. If people could entertain a discussion like the one you present which is level-headed and focused on objective (albeit hypothetical) points, we might have world peace right now 😅

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  @miroslavdyer-wd1ei6 күн бұрын

  this guy is good. I recommend him. He is about 1.5 Sarrano (sorry, Louis!!)

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  @amaurylaine20936 күн бұрын

  Best Explanation of the ROC curve I have seen on KZread so far!