Singular Value Decomposition for Dimensionality Reduction in Python
Last Updated on August 18, 2020
Reducing the number of input variables for a predictive model is referred to as dimensionality reduction.
Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data.
Perhaps the more popular technique for dimensionality reduction in machine learning is Singular Value Decomposition, or SVD for short. This is a technique that comes from the field of linear algebra and can be used as a data preparation technique to create a projection of a sparse dataset prior to fitting a model.
In this tutorial, you will discover how to use SVD for dimensionality reduction when developing predictive models.
After completing this tutorial, you will know:
- Dimensionality reduction involves reducing the number of input variables or columns in modeling data.
- SVD is a technique from linear algebra that can be used to automatically perform dimensionality reduction.
- How to evaluate predictive models that use an SVD projection as input and make predictions with new raw data.
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