MS-DS Master of Data science Master of Data Science 1

Question 1

Which ensemble learning method trains multiple models sequentially, where each model attempts to correct the errors made by the previous one?

Accepted Answer

Boosting

Answer

Boosting trains models sequentially, with each subsequent model focusing on the misclassified examples from the previous model. Bagging trains models independently in parallel, Random Forest is a specific bagging method, and stacking combines predictions using a meta-learner.

Question 2

In Principal Component Analysis (PCA), what do the eigenvalues of the covariance matrix represent?

Accepted Answer

The amount of variance explained by each principal component

Answer

Eigenvalues represent the variance explained by each principal component. The eigenvectors (not eigenvalues) represent the directions of maximum variance. This is why eigenvalues are used to decide how many components to retain.

Question 3

What distinguishes stochastic gradient descent (SGD) from batch gradient descent?

Accepted Answer

SGD updates model parameters using one (or a few) training examples per iteration rather than the entire dataset

Answer

SGD updates weights using a single sample (or mini-batch) at a time, making each update faster and introducing noise that can help escape local minima. Batch gradient descent uses the full dataset for each update, computing the true gradient but at higher computational cost.

Question 4

In the context of a Master of Data Science program, what does 'data leakage' during model training refer to?

Accepted Answer

When information from the test set inadvertently influences the training process

Answer

Data leakage occurs when information outside the training data (typically from the validation or test set) is used to build the model, resulting in overly optimistic performance estimates that do not generalize. This is a critical pitfall in real-world data science workflows.

Question 5

Which probability theorem is foundational to generative classifiers like Naive Bayes, allowing posterior class probabilities to be computed from likelihoods and priors?

Accepted Answer

Bayes' Theorem

Answer

Bayes' Theorem relates the posterior probability P(class|data) to the likelihood P(data|class) and the prior P(class). Naive Bayes classifiers apply this with the 'naive' assumption of conditional feature independence to make computation tractable.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Master of Data Science 1