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 |
A metric is a way to evaluate the performance of a machine learning model. Depending on the task, different metrics may be used.
0
votes
MSE relevance as a metric when errors < 1
If your only concern is small error values, why not simply scale the output by some constant?
The idea would be to multiply all the actual values by some factor e.g. 10*y_actual
Next, train your mode …