- We know by now that PCA can reduce the data while representing original data, thus making faster learning algorithm.
- Still, choose where to apply PCA wisely, not all area can be benefit from PCA

- We can use PCA for speedup our supervised learning algorithm.
- For this particular example, let's say we want to reduce the dimension of the data, down to one tenth of the original
- Extract only x-value from the original, so it become unlabeled dataset
- Then apply Â the new x-index(z-value) to coressponding y-value so it become the new training set
- Replace x with z, and run the hyphothesis.
- As warning above, use PCA Ureduce only for training set. Then use same mapping to cv and test set.
- For many problem max is 1/10 for keeping most retain

- Choose k wisely
- only use k = 2/3 for visualization

- Here we see how we misuse PCA for overfitting, for solving overfitting problem.

- y-value doesn't incorporated Â for PCA to take into account.
- Thus give a bad compression for representing original data that should take y-value as an input, resulting in throwing away some valuable information
- Regularization would works just fine, less likely throw away some valuable information for logistic regression or neural networks

- Adding PCA is a more complicated
- If data too large, or anything else, ex doesn't work. then use PCA. But not recommended to use PCA as a basic plan/first plan. Only if things doesn't work. PCA is a little complicated that shouldn't be at first plan for reducing the data

- PCA is really benefit for appropriate application
- Use PCA for compression,reducing memory usage, visualization
- PCA should be implemented wisely