Building Machine Learning Systems with Python - Third Edition

Applying PCA

Let's consider the following artificial dataset, which is visualized in the following left-hand plot diagram:

>>> x1 = np.arange(0, 10, .2)
>>> x2 = x1+np.random.normal(loc=0, scale=1, size=len(x1))
>>> X = np.c_[(x1, x2)]
>>> good = (x1>5) | (x2>5) # some arbitrary classes
>>> bad = ~good  

Scikit-learn provides the PCA class in its decomposition package. In this example, we can clearly see that one dimension should be enough to describe the data. We can specify this using the n_components parameter:

>>> from sklearn import linear_model, decomposition, datasets
>>> pca = decomposition.PCA(n_components=1)  

Also, here, we can use ...