Doing dimensionality reduction with manifolds – t-SNE
Getting ready
This is a short and practical recipe.
If you read the rest of the chapter, we have been doing a lot of dimensionality reduction with the iris dataset. Let's continue the pattern for additional easy comparisons. Load the iris dataset:
from sklearn.datasets import load_iris
iris = load_iris()
iris_X = iris.data
y = iris.target
Load PCA and some classes from the manifold module:
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE, MDS, Isomap
#Load visualization library
import matplotlib.pyplot as plt
%matplotlib inline
