Search Results
Search type | Search syntax |
---|---|
Tags | [tag] |
Exact | "words here" |
Author |
user:1234 user:me (yours) |
Score |
score:3 (3+) score:0 (none) |
Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
Views | views:250 |
Code | code:"if (foo != bar)" |
Sections |
title:apples body:"apples oranges" |
URL | url:"*.example.com" |
Saves | in:saves |
Status |
closed:yes duplicate:no migrated:no wiki:no |
Types |
is:question is:answer |
Exclude |
-[tag] -apples |
For more details on advanced search visit our help page |
This tag is meant to be used for questions related to how to evaluate a model performance, not only based on standard metrics, but also in the context of real use case applications. What is a good model might depend on many factors to take into account, to eventually get really useful data science applications.
4
votes
1
answer
2k
views
Choose ROC/AUC vs. precision/recall curve?
I am trying to get a clear understanding on various classification metrics, including knowing when to choose ROC/AUC as opposed to opting for the Precision/Recall curve.
I am reading Aurélien Géron's …
3
votes
2
answers
2k
views
Uncertainty about shape of ROC curve
I am working on a binary classification and the plotted ROC curves that I am using for evaluation together with AUC, have seemed strange to me. Here is an example.
I understand that ROC is a visual r …
4
votes
Uncertainty about shape of ROC curve
Oops. I found the reason!
The shape of ROC returned by the roc_curve depends on the number of unique values that are input to roc_curve. In my case I was getting only 3 points on the ROC curve. The mi …