DSE Data Science 5

Question 1

What does the term 'hyperparameter' refer to in machine learning?

Accepted Answer

Configuration settings set before training that control the learning process

Answer

Hyperparameters like learning rate, tree depth, and number of layers are set by the practitioner, not learned from data.

Question 2

Which of the following best describes transfer learning?

Accepted Answer

Reusing a pretrained model's learned representations for a new but related task

Answer

Transfer learning fine-tunes a model pretrained on a large dataset (e.g., ImageNet) for a specific downstream task.

Question 3

What is the role of the kernel in a Support Vector Machine (SVM)?

Accepted Answer

It maps data into a higher-dimensional space to find a linear separating hyperplane

Answer

Kernel functions compute dot products in a transformed feature space without explicitly computing the transformation.

Question 4

In Bayesian inference, what does the prior distribution represent?

Accepted Answer

Beliefs about the parameters before observing any data

Answer

The prior encodes existing knowledge or assumptions about a parameter before data is taken into account.

Question 5

What is the main difference between bagging and boosting ensemble methods?

Accepted Answer

Bagging trains models in parallel on bootstrap samples; boosting trains sequentially, focusing on errors

Answer

Bagging reduces variance by averaging parallel models, while boosting reduces bias by iteratively correcting mistakes.

DSE - Data Science Practice Test