Efficient models: reduce dimensionality with LDA in Python
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Learn how to reduce dimensionality and improve model efficiency using Linear Discriminant Analysis (LDA) in Python. Discover how LDA helps with classification problems by maximizing class separability while reducing noise in high-dimensional data.
At a Glance
Understand and implement Linear Discriminant Analysis (LDA), one of the best ML methods for dimensionality reduction in classification tasks. Dimensionality reduction is a fundamental machine learning technique that is frequently used to improve the performance of prediction models, interpretability, and data visualization. This easy-to-follow, hands-on project walks you through understanding LDA, when it’s most useful, and how to implement this dimensionality reduction technique using Python.
A Look at the Project Ahead
- Learn how LDA works
- Plot the LDA decision boundary for a binary classification problem
- Use LDA for classification
- Use LDA for dimensionality reduction
- Learn how to implement LDA using Python
What You’ll Need
While having a basic grasp of statistics, data science, and/or machine learning is helpful for following along, it’s not strictly required. The project is designed to be as accessible as possible to a general audience, with explanations primarily delivered in a graphical and intuitive manner. Whether you’re a beginner just starting out, or a seasoned professional looking for a refresher on LDA, this hands-on project is for you!
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