Practice Unsupervised Learning – Clustering & Dimensionality Reduction (6)

Practice: Unsupervised Learning – Clustering & Dimensionality Reduction

Lesson 6Unsupervised Learning – Clustering & Dimensionality Reduction

Practise this section

11 questions

Flashcard drill
5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
5 questions · about 3 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What is unsupervised learning?
Show hintHide hint
Think about whether the data has labels or not.
Question 2.
Name one clustering algorithm.
Show hintHide hint
What is a basic method to group similar data points?
3Question 3.
What is the main purpose of unsupervised learning?
- Predict outcomes
- Find hidden patterns
- Optimize models
Show hintHide hint
What do you want to learn from the data?
4Question 4.
True or False: K-Means clustering requires knowing the number of clusters in advance.
- True
- False
Show hintHide hint
Consider if the algorithm can determine clusters itself.
Question 5.
You have a dataset with 1000 movie reviews, each with features such as genre, rating, and user scores. Use clustering to segment the reviews into groups based on similarity, explain which algorithm you would choose and why.
Show hintHide hint
Consider the characteristics of your data and efficiency.
Question 6.
You have a high-dimensional dataset and want to visualize it. Explain how you would apply t-SNE and what its advantages are in this scenario.
Show hintHide hint
Think about how t-SNE optimizes clusters visually.

7 more questions in this section. Enroll free to practise them all.