MS-DS Master of Data science FREE Master of Data Science Machine Learning Questions and Answers 3

Question 1

Which dimensionality reduction technique preserves the global variance structure of the data by projecting onto orthogonal axes?

Accepted Answer

Principal Component Analysis (PCA)

Answer

t-SNE

Answer

UMAP

Answer

Autoencoders

Question 2

What problem does the vanishing gradient issue cause during neural network training?

Accepted Answer

Earlier layers learn extremely slowly because gradients become negligibly small

Answer

The model overfits to the training data

Answer

The learning rate becomes too large

Answer

The network produces identical outputs for all inputs

Question 3

Which evaluation metric is most appropriate when the cost of false negatives is significantly higher than false positives?

Accepted Answer

Recall

Answer

Precision

Answer

Accuracy

Answer

F1 Score

Question 4

What is the key assumption of the Naive Bayes classifier?

Accepted Answer

All features are conditionally independent given the class label

Answer

The data follows a normal distribution

Answer

The decision boundary is linear

Answer

Feature values must be continuous

Question 5

In K-means clustering, how are cluster centroids updated during each iteration?

Accepted Answer

By computing the mean of all points currently assigned to each cluster

Answer

By selecting the median point in each cluster

Answer

By choosing the point closest to all other points in the cluster

Answer

By randomly reassigning centroids within cluster boundaries

Question 6

Which technique helps address class imbalance in a binary classification dataset?

Accepted Answer

SMOTE (Synthetic Minority Over-sampling Technique)

Answer

Increasing the learning rate

Answer

Adding more hidden layers

Answer

Using a larger batch size