Singular value decomposition
In the last exercise, you saw how useful PCA could be in reducing the dimensions of a dataset when you're given a question about high dimensionality in a machine learning interview.
In this exercise, you'll practice SVD on the diabetes. This particular transformer can work with sparse matrices efficiently, as opposed to PCA, and performs linear dimensionality reductions by way of truncated singular value decomposition.
Recall that singular value decomposition takes the original data matrix, decomposes it into three matrices and uses them to calculate and return singular values.
Same place in the pipeline with a different technique:
This exercise is part of the course
Practicing Machine Learning Interview Questions in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import module
from ____.____ import ____
# Feature matrix and target array
X = ____.____('____', axis=1)
y = ____['____']