Machine Learning Fundamentals: Model Selection and Overfitting

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16 Questions

What is the purpose of model selection in machine learning?

To find the best model and optimize hyper parameters

What is the main difference between Supervised and Unsupervised learning?

Supervised learning has labels for training examples, while Unsupervised learning does not

What is the process of clustering in machine learning?

The process of grouping a set of objects into classes of similar objects

Why does both validation error and testing error increase as the validation set increases during model selection?

Because the training error increases

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

Clustering, Anomaly Detection, Dimensionality Reduction

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

Select Clusters centroids randomly

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

What does K-means Step 4: 2 involve?

Re-estimating the centroids positions

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

Until the positions of centroids are unchanged

What type of algorithm is K-means considered as?

Partitioning clustering algorithm

What property ensures that K-means typically converges quickly?

Monotonic decrease in each cluster's sum of squared distances

What does K-means Step 4: 1 involve?

Re-computing the average of all points in the same cluster without including the centroids

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

Assigning each point to its closest centroid

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

To handle non-globular shapes of clusters

Why does K-means typically converge quickly?

Because it assigns each point to its closest centroid at every iteration

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

Number of clusters

This quiz covers the fundamental concepts of machine learning, including the machine learning process, unseen data, ML algorithms, model selection, overfitting, and underfitting. It also delves into the significance of model selection in finding the best hypothesis and optimizing hyperparameters.

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