Feature Engineering Made Easy

SVD versus PCA components

Before we move on with our hotel data, let's do a quick experiment with our iris data to see whether our SVD and PCA really give us the same components:

1. Let's start by grabbing our iris data and creating both a centered and a scaled version:

# import the Iris dataset from scikit-learnfrom sklearn.datasets import load_iris# load the Iris datasetiris = load_iris()# seperate the features and response variableiris_X, iris_y = iris.data, iris.targetX_centered = StandardScaler(with_std=False).fit_transform(iris_X)X_scaled = StandardScaler().fit_transform(iris_X)

2. Let's continue by instantiating an SVD and a PCA object:

# test if we get the same components by using PCA and SVDsvd = TruncatedSVD(n_components=2)pca = PCA(n_components=2) ...