Reducing dimensionality with PCA

Now it's time to take the math up a level! PCA is the first somewhat advanced technique discussed in this book. While everything else thus far has been simple statistics, PCA will combine statistics and linear algebra to produce a preprocessing step that can help to reduce dimensionality, which can be the enemy of a simple model.

Getting ready

PCA is a member of the decomposition module of scikit-learn. There are several other decomposition methods available, which will be covered later in this recipe. Let's use the iris dataset, but it's better if you use your own data:

from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline

iris = datasets.load_iris()
iris_X = iris.data
y = iris.target

How to do it...

1. Import the decomposition module:

from sklearn import decomposition

2. Instantiate a default PCA object:

pca = decomposition.PCA()
pca

PCA(copy=True, iterated_power='auto', n_components=None, random_state...