AI AI Engineer: Machine Learning Fundamentals and Algorithms 2

Question 1

What is the kernel trick in Support Vector Machines (SVM)?

Accepted Answer

A technique that implicitly maps data to a higher-dimensional space to find a linear separator

Answer

The kernel trick computes dot products in a higher-dimensional feature space without explicitly transforming the data, enabling SVMs to find linear decision boundaries for data that is nonlinearly separable in the original space.

Question 2

What is the primary difference between gradient boosting and bagging (e.g., Random Forest)?

Accepted Answer

Gradient boosting builds models sequentially where each model corrects the errors of the previous one

Answer

Gradient boosting builds an ensemble sequentially, where each new model focuses on correcting the residual errors of the combined previous models, reducing bias — unlike bagging which builds models independently in parallel to reduce variance.

Question 3

Why is feature standardization (zero mean, unit variance) important before applying algorithms like SVM or k-nearest neighbors?

Accepted Answer

It prevents features with larger numerical ranges from dominating distance-based calculations

Answer

Distance-based algorithms are sensitive to feature scale — a feature ranging 0–10,000 will dominate a feature ranging 0–1 in distance calculations, so standardization puts all features on equal footing.

Question 4

What does Principal Component Analysis (PCA) accomplish?

Accepted Answer

It reduces dimensionality by projecting data onto orthogonal axes of maximum variance

Answer

PCA finds orthogonal principal components (linear combinations of original features) ordered by the amount of variance they explain, allowing dimensionality reduction by keeping only the top components.

Question 5

In k-means clustering, how is the optimal number of clusters (k) typically determined?

Accepted Answer

By using the elbow method, which plots inertia vs. k and identifies the point of diminishing returns

Answer

The elbow method plots the within-cluster sum of squares (inertia) against different values of k — the 'elbow' point where adding more clusters yields diminishing reductions in inertia suggests the optimal k.

AI - Engineer Practice Test

AI - Engineer Practice Test

AI AI Engineer: Machine Learning Fundamentals and Algorithms 2