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 | |
Results tagged with least-squares
Search options not deleted
user 99438
Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of that observation conditioned on the parameter value. Gaussian linear models are fit by least squares and least squares is the idea underlying the use of mean-squared-error (MSE) as a way of evaluating an estimator.
10
votes
2
answers
612
views
Negative R2 on Simple Linear Regression (with intercept)
I am doing a simple Linear Regression (with intercept) which ends up presenting a negative R2, this should not be possible (cf comment 2 at the end)
Reproducible examples of the issue:
Minimal sklear …
- 346
11
votes
Accepted
Negative R2 on Simple Linear Regression (with intercept)
Detailed explanation of the problem:
In the case of X being near-singular (high colinearity/covariance between features), different issues where coming both from scipy.linalg.lstsq() and sklearn.linea …
- 346