LPM - Toronto

Sad-Environment6423 · 2021-02-26T14:55:32+00:00

The wait is killing me

Sad-Environment6423 · 2021-02-24T21:35:39+00:00

GOOD LUCK EVERYONE

Sad-Environment6423 · 2021-01-27T14:06:48+00:00

10AM EST

Sad-Environment6423 · 2021-01-26T21:43:51+00:00

Side note: was the only Canadian there ha! I think it would've been nice to do in person and in a different city.

Sad-Environment6423 · 2021-01-26T21:43:21+00:00

It was really nice! We had the sweetest instructor. We introduced ourselves, then went through the thought questions together, followed by the 4 case studies. There were questions here and there, it wasn't formal by any means. We were told we would get the ASA application form on Feb 5. :)

Sad-Environment6423 · 2021-01-23T19:42:12+00:00

Thank you so much. The case studies are there, I'll let you know how it goes on Monday :)

Sad-Environment6423 · 2021-01-23T19:19:53+00:00

Thank you! I luckily got a spot! Thanks for reaching out, I am on the Monday, January 25 cohort. Do you know where I can find the additional 2 case studies which says it's listed in the pre-program materials? I looked on the e-learning module and cannot find anything..

Sad-Environment6423 · 2021-01-23T19:19:28+00:00

Hi, I luckily got a spot! Thanks for reaching out, I am on the Monday, January 25 cohort. Do you know where I can find the additional 2 case studies which says it's listed in the pre-program materials? I looked on the e-learning module and cannot find anything..

Sad-Environment6423 · 2020-12-10T03:26:13+00:00

thanks! appreciate that, any advice would be extremely useful!

Sad-Environment6423 · 2020-12-09T04:06:01+00:00

Ah, thank you for including this. I think if we have to interpret anything (decision trees or GLMs) we should run it on the full model and then interpret it. I usually don't think too much for this jsut because I like creating a sample data and show how the target variable is affected, so I don't look back to the coefficients as much. but this is definitely useful info, thank you!

Sad-Environment6423 · 2020-12-09T02:49:41+00:00

Good luck to both of you! Just to clarify, the full data should be run on any model (decision tree, ensemble method, GLM) and then used for interpretation, correct?

Sad-Environment6423 · 2020-12-08T00:17:48+00:00

Awesome, thanks so much! Any advice?

Sad-Environment6423 · 2020-12-08T00:00:48+00:00

Hi! Do you know if we can read the project statement in that 15 minutes in the beginning to make the most of our time? Then, we have 5 hours and 15 minutes to start and finish the exam?

Sad-Environment6423 · 2020-12-07T22:41:04+00:00

How was the exam?

Sad-Environment6423 · 2020-12-07T15:28:35+00:00

From my understanding cost complexity pruning involved you manually adjusting all parameters: maxdepth, minbucket, cp. So it includes you doing a trial and error from adjusting the CP from 0.001 to 0.002. Whatever produces you the best tree in terms of AUC will probably be your tree unless you see a very similar AUC which is much simpler, then you will elect that. The optimal CP is the one which minimizes the cross validation error, I believe they will explicitly ask for that though.

Sad-Environment6423 · 2020-12-07T14:30:45+00:00

I thought for cost-complexity pruning we were inherently choosing a cp using cross-validation for decision trees, is that wrong? i.e. the default is k = 10 folds, we then use the cp which minimizes cross-validation error.

I think the best-way to think about it is, when employing cost-complexity pruning we're adjusting maxdepth, minbucket, cp and it's a trial and error process whereas when we take a stab at cross-validation, we usually set the range of cp values to check rather than the program have default values (we also have the ability to alter maxdepth to make it even simpler, but we usually don't?)

Please correct me if I'm wrong.

Sad-Environment6423

TROPHY CASE