Let's apply principal component analysis to the Iris dataset. This is a 4D dataset that we're going to reduce down to 2 dimensions. We're going to see that we can actually still preserve most of the information in that dataset, even by throwing away half of the dimensions. It's pretty cool stuff, and it's pretty simple too. Let's dive in and do some principal component analysis and cure the curse of dimensionality. Go ahead and open up the PCA.ipynb file.
It's actually very easy to do using scikit-learn, as usual! Again, PCA is a dimensionality reduction technique. It sounds very science-fictiony, all this talk of higher dimensions. But, just to make it more concrete and real again, a common application is image compression. You can think of an image of a black and white picture, as 3 dimensions, where you...
