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 regression
Search options not deleted
user 331303
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
3
votes
1
answer
189
views
Resources for learning about lasso
I'm looking for resources on lasso regression at the undergraduate mathematics level. All I can find is a lot of complex texts on variable selection, concentration inequalities, etc. …
2
votes
1
answer
89
views
Making my linear regression model meet assumptions causes a large increase in mean squared e...
I was creating a linear regression model on a particle collisions dataset. … I made use of a train-test split during my experiments and here is my methodology using multiple linear regression with no regularisation. …
- 216
1
vote
1
answer
367
views
Effect of basis functions on the dimension of a linear regression model
In Scikit-learn I can use polynomial features to create polynomial linear regression models. Scikit-learn transforms my original data as follows. … How do we construct the linear regression model taking into account that vector space? …
- 216
0
votes
0
answers
27
views
Interpretation of lasso shrinkage
In the case of the ridge estimator, we can interpret the shrinkage induced by the ridge estimator to be at its most extreme when a predictor has low variance. High-variance predictors provide the most …
- 216
0
votes
2
answers
61
views
Calculating the distribution of the sum of the squares of the predictors in linear regression
I'm calculating the distribution of the sum of the squares of the components of the MLE $\hat{\beta}$ in linear regression with normal errors. We are assuming that $\beta = 0$. …
- 216