Maximizing Class Separability with LDA

LDA is a powerful dimensionality reduction technique that is particularly useful for supervised learning tasks, especially in classification problems. Unlike PCA, which focuses on maximizing variance across all data points without considering class labels, LDA aims to maximize class separability.

Getting ready

We will use the same Wine dataset used previously, so we do not have to load it again.

How to do it…

As we saw with PCA, LDA only requires loading a single scikit-learn class to perform it on your dataset. We will also be using the Pipeline() class to string together our scaling prior to applying LDA.

1. Load libraries:

   from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

2. Split wine dataset by features and target:

   X_wine, y_wine = wine.data, wine.target

3. Create LDA pipeline for wine dataset:

   lda_pipeline_wine = Pipeline([
       (‘scaler’, StandardScaler()),
       (‘lda’, LinearDiscriminantAnalysis...