Machine Learning Fundamentals: Model Selection and Overfitting
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Questions and Answers

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?

    <p>Because the training error increases (B)</p> Signup and view all the answers

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

    <p>Clustering, Anomaly Detection, Dimensionality Reduction (C)</p> Signup and view all the answers

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

    <p>Select Clusters centroids randomly (C)</p> Signup and view all the answers

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

    <p>To compute the average of all points in the same cluster (D)</p> Signup and view all the answers

    What does K-means Step 4: 2 involve?

    <p>Re-estimating the centroids positions (A)</p> Signup and view all the answers

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

    <p>Until the positions of centroids are unchanged (D)</p> Signup and view all the answers

    What type of algorithm is K-means considered as?

    <p>Partitioning clustering algorithm (B)</p> Signup and view all the answers

    What property ensures that K-means typically converges quickly?

    <p>Monotonic decrease in each cluster's sum of squared distances (D)</p> Signup and view all the answers

    What does K-means Step 4: 1 involve?

    <p>Re-computing the average of all points in the same cluster without including the centroids (C)</p> Signup and view all the answers

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

    <p>Assigning each point to its closest centroid (B)</p> Signup and view all the answers

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

    <p>To handle non-globular shapes of clusters (A)</p> Signup and view all the answers

    Why does K-means typically converge quickly?

    <p>Because it assigns each point to its closest centroid at every iteration (A)</p> Signup and view all the answers

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

    <p>Number of clusters (B)</p> Signup and view all the answers

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