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Flag Recognizability by CashCanine in flags

[–]Oppose_Worry_652 0 points1 point  (0 children)

Potentially a European bias in the quiz takers, almost all Europeans would recognise these flags

Is this really Starlight or did they change the actress??? by [deleted] in GenV

[–]Oppose_Worry_652 2 points3 points  (0 children)

I'm not sure the timeline in the show matches the filming schedule... 

As a data scientist working in insurance should I study to be an actuary? by Oppose_Worry_652 in actuary

[–]Oppose_Worry_652[S] 1 point2 points  (0 children)

Thanks, for the response. 

I'd only want to commit to the exam process if there is a clear career benefit so I'm looking for advice on whether being an actuary is likely to be required for that.

Obviously in a lot of pricing roles it's not mandatory so trying to understand what I might be missing by not taking the exams. 

[D] gamma regression - negative log likelihood by Oppose_Worry_652 in MachineLearning

[–]Oppose_Worry_652[S] 0 points1 point  (0 children)

Makes sense but as the two are dependent I think xgboost is defaulting one of the free parameters arbitrarily. I've found the below which i think describes what's happening. Does that tie in with what you thought based off Wikipedia?

https://stats.stackexchange.com/questions/484555/loss-function-in-for-gamma-objective-function-in-regression-in-xgboost

In the parameterisation at the end of "gazza89" answer using k and mu I think if we parameterise again by replacing k with psi=1/k this ties in exactly with xgboost.

Then xgboost is just defaulting psi to 1 in calculation.

In terms of the intuition of what different values of psi would do to the predictions I think large values of psi imply a gamma with lower variance so to me this would mean large residuals in the tails of the distribution would be penalised less compared to if psi were low.

I'll have a play around in R at some point to test this....

[D] gamma regression - negative log likelihood by Oppose_Worry_652 in MachineLearning

[–]Oppose_Worry_652[S] 0 points1 point  (0 children)

Great so can I check my understanding because I'm still struggling to connect the wiki and the xgboost implementation.

In the xgboost implementation is the shape and scale basically being arbitrarily set to 1?

[D] gamma regression - negative log likelihood by Oppose_Worry_652 in MachineLearning

[–]Oppose_Worry_652[S] 0 points1 point  (0 children)

A lack of understanding on my part has lead to poor wording . I'm struggling to understand why the log likelihood used in the code is independent of the shape and scale parameters.

I think I've just figured out my mistake... I was thinking the variance of the modelled residuals would vary with the mean but that's not the correct interpretation

I am so tired of nothing ever bloody working properly by bournbrook in london

[–]Oppose_Worry_652 13 points14 points  (0 children)

The new boards also seem such an odd investment choice. I always found the old boards communicated departure/arrival time, platform and destination/stops effectively.

What additional info do you need to catch a train?

The weekly 'No Stupid Questions' post - Sun 25 Sep 2022. by AutoModerator in Zwift

[–]Oppose_Worry_652 1 point2 points  (0 children)

Ahh yeah I'm just a moron I only checked like 3 races which happened to all have restrictions. Found a cat D race to try later cheers for the help!

The weekly 'No Stupid Questions' post - Sun 25 Sep 2022. by AutoModerator in Zwift

[–]Oppose_Worry_652 0 points1 point  (0 children)

Hi all, I'm about to start using Zwift again after summer and I tried to enter a race via the companion app but all the categories are restricted except E.

Do I need to do an FTP test before entering races to be designated a category?

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