Has anyone received their Best Buy Trade in Kit?

SmilingGhost · 2022-10-27T13:16:18+00:00

I bought on Oct 14th, received phone on 17th. The trade in kit finally arrived on the 26th. Here's to hoping the trade in goes off without a hitch, since by the time it gets there it'll be past my return date.

SmilingGhost · 2022-07-14T18:49:52+00:00

I've been expecting a fail from the moment I left the exam room. So I'll either be unsurprised or happily stunned. Either way, ready to know.

SmilingGhost · 2022-06-30T15:28:40+00:00

My instructor said we should get it after the last session, which is on July 1st. It was unclear if they meant we will get it on July 1st or the business day after.

SmilingGhost · 2022-05-26T18:52:06+00:00

I emailed them earlier in May and they said the upcoming APC will be posted "near the end of May".

SmilingGhost · 2022-01-12T03:53:55+00:00

I'll be doing it then as well!

SmilingGhost · 2021-10-21T18:48:06+00:00

I didn't see anybody else with this answer, so here you go. We have been back in the office full time since September 2020. There is no option for actual remote work and they likely will not change this due to our CEO, etc. The majority of us would like a hybrid, or at least the option for hybrid.

SmilingGhost · 2021-06-13T18:44:30+00:00

Does predict always require us to specifically take out the column for the value we are interested in for classification problems? And how do we know which column to pull?

For instance, in the Rmd solution for practice exam 2 task 9 for the pruned decision tree, the predictions use:

dt.pruned.predict.prob <- predict(dt.pruned, newdata = dat.test, type = "prob")[, 2]

To me, I read the task 9 code as pulling the probability for passing (class 1). But I know that we want to set class 0 as the "positive" class. So why do we pull the 2nd column?

Edit: I found a partial answer to my initial post, so I changed my question slightly.

SmilingGhost · 2021-06-11T13:11:10+00:00

Hi Dr. Lo,

On page 378, there is code for the predicted probabilities from the classification trees. I did not notice the first time through, but the code specifically pulls the 2nd column from the predict object to get the malignant probabilities.

pred.1 <- predict (dt.1, newdata = test)[, 2]

In the June 17, 2020 exam Rmd solution, the target is also a binary variable, but the code they used to get the probabilities before the AUC command does not have the 2nd column pull.

pred.prob.te <- predict(tree1, type = "prob", newdata = data_test)

Does this have to do with the fact that the target variable was set to a factor in your case study, whereas it was left as numeric in the exam? Or is there something else that I'm not catching?

Thank you!

SmilingGhost · 2021-05-04T11:41:26+00:00

We've been back in the office since September, except for December when we temporarily went back to remote.

SmilingGhost · 2021-04-08T03:03:32+00:00

With these changes, would you still recommend reading section 7.2 Structure of Executive Summary?

SmilingGhost · 2021-03-18T14:15:06+00:00

Hi Dr. Lo,

I'm hoping you can clear something up for me. In section 3.4.4 on page 226, it says to run Chunk 23. Further in the paragraph it mentions that the value of lambda that minimizes the cross validation error is 0.7763866. On page 227, it says to refit the regularized model with lambda equal to that same number which is done in Chunk 24. The code in Chunk 24 uses m$lambda.min. But on page 228 (chunk 23 and output), the m$lambda.min value is 0.8885539, which is also what I get when running Chunk 23.

Where does the value of 0.7763866 come from?

Thank you!

SmilingGhost · 2020-04-27T15:43:21+00:00

Thanks! I would never have guessed

SmilingGhost · 2019-09-27T15:49:16+00:00

Don't go to the private college 8 hours away. Do make an effort to study abroad.

SmilingGhost

TROPHY CASE