Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: Supervised PCA and Fishers Linear D

The video tutorial discusses Fisher's linear discriminant analysis (LDA) as a supervised dimensionality reduction technique. It guides viewers to explore the Scikit-learn library for implementing LDA without starting from scratch. The tutorial emphasizes applying LDA on classification datasets to observe the relationship between reduced dimensions and the number of classes. The activity encourages practical exploration and understanding of LDA's impact on data classification.

Can you describe the process of reducing dimensions using FLD on a dataset?

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What activities are suggested to explore the relationship between reduced dimensions and classes in a dataset?

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