
#301




I used cobalt at first, but my work monitors have this stupid adaptive brightness feature that can't be disabled, and it makes it very hard to see the blue text for comments when the screen dims.
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#302




Is anybody else at a point where they feel good about the material, but are very worried about the fact we only have five hours to complete the exam? Like I know when I took LTAM, the strategy for the written portion was even if you didn't know the problem 100%, you could just write down what you do know and move one. But for this exam, it seems like each task builds on one another, so that strategy isn't exactly applicable here. I feel like I'm going to run into a roadblock and am never going to have enough time to finish the rests of the tasks.
Anyway, if you are feeling like this, how are you planning on prepping over the next week and 2 days? 
#303




Quote:
I didn't make the effective adjustments to my data/features during the December exam so my model was pretty bad, and I kept going back and trying to tweak things when I should've focused on the writing portion. If your model sucks or if you get stuck on a task, you need to have the presence of mind to realize that you'll salvage more points by writing about its limitations/constraints than trying to fix it after a certain point. Example: Task 4 (Select an interaction) from the Hospital Readmissions project. If you end up exploring all of the feature pairs and can't see anything, just commit to an interaction that appears plausible and move on. 
#304




Quote:
For a log link, say we have E(Y) = e^(b0 + b1*x1) Then E(Yx=0) = e^(b0) and E(Yx=1) = e^(b0 + b1) = E(Yx=0)*e^(b1) This is where the multiplicative effect comes into play for a log link function, but I don't believe you can say the same for a logit link function. 
#305




Quote:
Given there are 10+ tasks, I'm not sure if I'm able finish the report in 22.5 hours under the exam condition. I tried the Hospital example and was barely able to finish writing in 2.5 hours... 
#306




I'm having a hard time understanding the error diagnostics output from the plot(model) function. Are these important to run for a regression model if you've already looked at RMSE and AIC? Are these useful at all for a classification problem?
Referring to the following: Residuals versus fitted plot Normal QQ plot ScaleLocation or Residuals versus Fitted plot Residuals versus Leverage 
#307




Does anyone understand cutoff value? In the student success problem, I understand it conceptually as if the student as probability of x of passing, mark it as P. (Please correct me if I'm wrong). In the solution however, it doesn't seem to have a good explanation behind why its .5. Because the accuracy is similar to the actual data?

#308




Quote:
on a different note, does anyone know why in all of the exercises and such we only use the caret method of "repeated cv" when doing ensemble methods, and we use normal "cv" for base models? 
#309




Quote:
log(p/1p) = XB, where p = probability of success. exp(coefficient) has a multiplicative effect on the odds ratio. Here is a nice example with factor and continuous predictors https://stats.idre.ucla.edu/other/mu...icregression/ 
#310




Quote:
However, there is something called the deviance residual (you can obtain this with residuals(glm1, type = "deviance")) and this is adjusts the raw residuals to the shape of your distribution and these will follow normal with constant variance if the data fits the distribution well. Note that these are no good for discrete distributions (binomial, poisson) so it wouldn't have been useful in any exam we have seen. 
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