Linear Discriminant Analysis 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.
Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique, providing a projection of a training dataset that best separates the examples by their assigned class.
The ability to use Linear Discriminant Analysis for dimensionality reduction often surprises most practitioners.
In this tutorial, you will discover how to use LDA 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.
- LDA is a technique for multi-class classification that can be used to automatically perform dimensionality reduction.
- How to evaluate predictive models that use an LDA projection as input and make predictions with new raw data.
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