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.

What is the primary purpose of Fisher's Linear Discriminant Analysis?

• To cluster data points into groups

• To increase the number of dimensions in a dataset

• To perform unsupervised learning

• To reduce the number of dimensions while preserving class separability

Which library is recommended for implementing Fisher's Linear Discriminant Analysis without starting from scratch?

• TensorFlow

• PyTorch

• Scikit-learn

• Keras

Which of the following is NOT a step in implementing FLD using Scikit-learn?

• Transforming the data to a lower-dimensional space

• Using Numpy to manually calculate discriminant functions

• Fitting the model to the data

• Importing the LinearDiscriminantAnalysis class

What is the relationship between the number of classes and the reduced dimensions in FLD?

• The number of reduced dimensions is one less than the number of classes

• The number of reduced dimensions is twice the number of classes

• The number of reduced dimensions is always equal to the number of classes

• There is no relationship between the number of classes and reduced dimensions

What type of datasets is FLD primarily used for?

• Clustering datasets

• Regression datasets

• Classification datasets

• Time series datasets