Practical Exercises in Dimensionality Reduction

Wrapping up the chapter, you will engage in practical exercises that involve applying PCA, LDA, and t-SNE to various datasets. These hands-on activities will reinforce the understanding of when and how to use each dimensionality reduction technique to enhance model efficiency and effectiveness.

Example 1: PCA with Logistic Regression

In this example, we will analyze how applying PCA before training a logistic regression model affects performance on the Iris dataset. We realize we haven’t covered Logistic Regression so this will be an exercise in pushing your boundaries!

Implementation steps:

1. Load libraries
2. Split data
3. Pipeline without PCA
4. Pipeline with PCA
5. Fit and evaluate both pipelines
6. Print results

Example 2: t-SNE for Visualization

In this example, we will use t-SNE for visualizing high-dimensional data from a more complex dataset (e.g., MNIST) while analyzing...