Testing methods to reduce dimensionality with pipelines

Here we will see how different estimators composed of dimensionality reduction and a support vector machine perform.

Getting ready

Load the iris dataset and some dimensionality reduction libraries. This is a big step for this particular recipe:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC, LinearSVC
from sklearn.decomposition import PCA, NMF, TruncatedSVD
from sklearn.manifold import Isomap
%matplotlib inline

How to do it...

1. Instantiate a pipeline object with two main parts:
   • An object to reduce dimensionality
   • An estimator with a predict method

pipe = Pipeline([
    ('reduce_dim', PCA()),
    ('classify', SVC())
])

2. Note in the following code that Isomap comes from the manifold module and that the non-negative matrix factorization (NMF) algorithm utilizes SVDs to break up a matrix into...