Timeline for How to use pca results for linear regression
Current License: CC BY-SA 3.0
3 events
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| Mar 6, 2018 at 22:27 | comment | added | Borhan Kazimipour | Not necessarily the first four columns of scores unless they are already sorted (descendent) based on the eigenvalues (that shows the variance in the new columns). Therefore, the four columns that you select should preserve most of the variations in the dataset. It's normal to see very different from the original observation as PCA tries to project the points to a new set of dimensions where the columns are orthogonal (linearly uncorrelated). Since your original dimensions may not be necessarily orthogonal, the input and output of the PCA may look very different. | |
| Mar 6, 2018 at 22:16 | comment | added | אבנר יעקב | that i understand, but what is exactly my four new variables? are they the first four columns of the "score"? because i get something really different from the originals observations. | |
| Mar 6, 2018 at 21:48 | history | answered | Borhan Kazimipour | CC BY-SA 3.0 |