Podcast
Questions and Answers
What is the purpose of model selection in machine learning?
What is the purpose of model selection in machine learning?
- To divide the dataset into training, validation, and testing sets
- To compute the Euclidian distance between each point and the clusters centroids
- To refine the clusters centroids positions
- To find the best model and optimize hyper parameters (correct)
What is the main difference between Supervised and Unsupervised learning?
What is the main difference between Supervised and Unsupervised learning?
- Supervised learning has labels for training examples, while Unsupervised learning does not (correct)
- Supervised learning does not require a validation set, while Unsupervised learning does
- Supervised learning involves clustering, while Unsupervised learning does not
- Unsupervised learning has labels for training examples, while Supervised learning does not
What is the process of clustering in machine learning?
What is the process of clustering in machine learning?
- The process of computing the Euclidian distance between each point and the clusters centroids
- The process of selecting clusters centroids randomly
- The process of grouping a set of objects into classes of similar objects (correct)
- The process of refining the clusters centroids positions
Why does both validation error and testing error increase as the validation set increases during model selection?
Why does both validation error and testing error increase as the validation set increases during model selection?
What is the main purpose of Unsupervised tasks in machine learning?
What is the main purpose of Unsupervised tasks in machine learning?
What is the first step in the K-means clustering algorithm?
What is the first step in the K-means clustering algorithm?
What is the purpose of re-assigning the clusters centroids positions in the K-means algorithm?
What is the purpose of re-assigning the clusters centroids positions in the K-means algorithm?
What does K-means Step 4: 2 involve?
What does K-means Step 4: 2 involve?
In K-means, what termination condition is used to end the algorithm?
In K-means, what termination condition is used to end the algorithm?
What type of algorithm is K-means considered as?
What type of algorithm is K-means considered as?
What property ensures that K-means typically converges quickly?
What property ensures that K-means typically converges quickly?
What does K-means Step 4: 1 involve?
What does K-means Step 4: 1 involve?
What is the role of K-means Step 4: 3?
What is the role of K-means Step 4: 3?
What is the main reason behind using Expectation Maximization (EM) algorithm in K-means?
What is the main reason behind using Expectation Maximization (EM) algorithm in K-means?
Why does K-means typically converge quickly?
Why does K-means typically converge quickly?
What does 'K' represent in K-means algorithm?
What does 'K' represent in K-means algorithm?
