These are chat archives for FreeCodeCamp/DataScience
discussion on how we can use statistical methods to measure and improve the efficacy of http://freeCodeCamp.com
The logistic regression gives you a very good idea of possible variables involved.
Good discussion! Only the following charts : "xG Distributions in Test Data" are in general unclear and not well discussed.
It would be nice to get a summary of your findings at the end. Example:
I would suggest to find out what are the shots that the model fails the most and suggest what you would improve?
Something about NN and optimization of gradient descent techniques: ADAM.
@mcbarlowe Yes. I was trying to find reasons why not to use it and didn't find many - the ROC is applicable to several cases. I was recently more into using statistics but in fact the use of ROC and AUC vs other methods is actually more a question of culture. For example, ROC is much used between those with medical background. It has its followers in Marketing too.
I have always liked it but for a moment I don't know why I started thinking it was applicable to some specific cases. It seems the number of cases is wider that I thought. I made a wrong assumption actually.
That's ok then. I will check when it is more applicable and try to use it more frequently. Thanks!
evaristoc sends brownie points to @mcbarlowe :sparkles: :thumbsup: :sparkles: