Practice - Dimensionality Reduction: Simplifying Complexity
Practice Questions
Test your understanding with targeted questions
What is the purpose of dimensionality reduction?
💡 Hint: Think about the challenges of high-dimensional data.
What does PCA stand for?
💡 Hint: Remember it is a method for linear dimensionality reduction.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the main goal of dimensionality reduction?
💡 Hint: Remember the outcomes of reducing features.
True or False: t-SNE is primarily used for dimensionality reduction of datasets intended for model input.
💡 Hint: Consider the main purpose of t-SNE.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Given a dataset with 500 features, you want to reduce it down to a more manageable number while retaining as much variance as possible. Explain how you would use PCA for this task, including key steps.
💡 Hint: Start by considering the necessity of standardization and covariance.
Consider the case of anomaly detection in a dataset with many varied features. Decide whether you would prefer feature selection or extraction to enhance performance. Justify your answer.
💡 Hint: Think about the nature of the data and the desired outcomes.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.