Timeline for Radiomics: Machine learning vs Classic Multivariate Statistics for small datasets and a lot of variables... which is better?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 12, 2020 at 11:53 | comment | added | EdM | @crlagos0 the use of PCA in that paper is different from how it's used in regression. Principal components (PCs) in morphometrics are interpreted functionally in terms of size (PC1) and shape (PC2, PC3). Stability of loadings of features onto PCs is needed for such interpretation. In regression, you use cross-validation to select a number of PCs that fits but doesn't overfit the relationships of features to outcome. No interpretations are assigned to particular PCs. Ridge regression smooths out the PCs rather than forcing a cutoff, providing further protection from overfitting. | |
| Jun 12, 2020 at 3:53 | comment | added | crlagos0 | I get your point perfectly, but I'm talking about another thing really with "stability of PCA" (it's not about the use of the principal components as variables for regression, sorry I'm not expressing well myself). There are some concerns in different scientific areas (like ecology) about performing PCA with a ratio < 5:1 of observations to variables. Here you can read an example of a discussion about "stability of PCA results " regarding to sample size. tandfonline.com/doi/abs/10.1577/T08-091.1 | |
| Jun 12, 2020 at 1:46 | comment | added | EdM | @crlagos0 with principal component regression the “number of variables” is effectively the number of components that are kept, not the original number of features submitted to PCA. So even if you start with 58 features. If you only retain the first 5 principal components from PCA that’s like choosing 5 features—but with the advantages noted in the answer. For many more features than observations, you should look into Lasso or elastic net; start with the text I linked in the answer. | |
| Jun 12, 2020 at 0:07 | comment | added | crlagos0 | Thanks a lot, great explanation. One doubt I have left is about stability of PCA in small datasets, I've been reading about a minimum needed ratio of observations to variables of at least 5 : 1, which my dataset is far from accomplishing (I've another project with 84 observations and 198360 variables, so a better understanding of this subject is pretty important to me) | |
| Jun 11, 2020 at 23:48 | vote | accept | crlagos0 | ||
| Jun 11, 2020 at 23:23 | history | answered | EdM | CC BY-SA 4.0 |