Machine Learning Fundamentals: Model Selection and Overfitting

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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?

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?

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?

Because the training error increases Signup and view all the answers

What is the main purpose of Unsupervised tasks in machine learning?

Clustering, Anomaly Detection, Dimensionality Reduction Signup and view all the answers

What is the first step in the K-means clustering algorithm?

Select Clusters centroids randomly Signup and view all the answers

What is the purpose of re-assigning the clusters centroids positions in the K-means algorithm?

To compute the average of all points in the same cluster Signup and view all the answers

What does K-means Step 4: 2 involve?

Re-estimating the centroids positions Signup and view all the answers

In K-means, what termination condition is used to end the algorithm?

Until the positions of centroids are unchanged Signup and view all the answers

What type of algorithm is K-means considered as?

Partitioning clustering algorithm Signup and view all the answers

What property ensures that K-means typically converges quickly?

Monotonic decrease in each cluster's sum of squared distances Signup and view all the answers

What does K-means Step 4: 1 involve?

Re-computing the average of all points in the same cluster without including the centroids Signup and view all the answers

What is the role of K-means Step 4: 3?

Assigning each point to its closest centroid Signup and view all the answers

What is the main reason behind using Expectation Maximization (EM) algorithm in K-means?

To handle non-globular shapes of clusters Signup and view all the answers

Why does K-means typically converge quickly?

Because it assigns each point to its closest centroid at every iteration Signup and view all the answers

What does 'K' represent in K-means algorithm?

Number of clusters Signup and view all the answers

Machine Learning Fundamentals: Model Selection and Overfitting

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