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I've got a test dataset with 4 features and the PCA produces a set of 4 eigenvectors, e.g.,

```
EigenVectors: [0.7549043055910286, 0.24177972266822534, -0.6095588015369825, -0.01000612689310429]
EigenVectors: [0.0363767549959317, -0.9435613299702559, -0.3290509434298886, -0.009706951562064631]
EigenVectors: [-0.001031816289317291, 0.004364438034564146, 0.016866154627905586, -0.999847698334029]
EigenVectors: [-0.654824523403971, 0.2263084929291885, -0.7210264051508555, -0.010499173877772439]
```

Do the eigenvector values represent the features from the original dataset? E.g., is feature 1 & 2 explaining most of the variance in eigenvector 1?

Am I interpreting the results correct to say that features 1 and 2 are therefore the most important in my dataset since PC1 represents 90% of the variance?

I'm trying to map back to the original features but am unsure how to interpret the results.