Q1. Linear Models
Which statement about linear models is correct?
A. They always produce nonlinear decision boundaries
B. They use a hyperplane as a decision surface
C. They maximise classification accuracy directly
D. They ignore input features
✅ Answer: B
👉 From PDF: “The decision surface is a hyperplane.”
Q2. Sum of Squared Errors (SSE)
What is the goal when training a linear model?
A. Maximise likelihood
B. Minimise SSE
C. Maximise distance between points
D. Minimise number of features
✅ Answer: B
👉 Linear models minimise SSE
Q3. Effect of Outliers
What is true about samples far from the hyperplane?
A. They have no effect
B. They are ignored
C. They have a stronger effect on the model
D. They reduce variance
✅ Answer: C
👉 “Samples far away from hyperplane have a stronger effect.”
Q4. Linear Separability
When are data points typically linearly separable?
A. When ( n \gg d )
B. When ( n \le d + 1 )
C. Always
D. Never
✅ Answer: B
👉 From PDF: separable when ( n \le d + 1 )
Q5. KNN Prediction
What does KNN use to classify a new data point?
A. Mean of all points
B. Nearest neighbours
C. Gradient descent
D. Covariance matrix
✅ Answer: B
👉 Based on nearest neighbours
Q6. Role of K in KNN
What is the effect of choosing a small K?
A. Smooth decision boundary
B. Linear boundary
C. Highly flexible / irregular boundary
D. No effect
✅ Answer: C
👉 Small K → complex, irregular decision surfaces
Q7. Distance Metrics
Which is NOT mentioned as a valid distance in KNN?
A. Euclidean distance
B. Manhattan distance
C. Hamming distance
D. Fourier distance
✅ Answer: D
👉 Others are listed in PDF
Q8. Curse of Dimensionality
What happens as dimensionality increases?
A. Less data is needed
B. Nearest neighbours become more meaningful
C. Required training data increases exponentially
D. Models become simpler
✅ Answer: C
👉 “Required amount of training examples increases exponentially”
Q9. Cross-Validation
What is the purpose of cross-validation?
A. Increase training error
B. Evaluate model performance on unseen data
C. Remove noise
D. Reduce dimensionality
✅ Answer: B
👉 Used for validation/testing
Q10. Accuracy Definition
Accuracy is defined as:
A. TP / FP
B. (TP + TN) / Total
C. FN / Total
D. TP / (TP + FN)
✅ Answer: B
👉 Standard accuracy formula
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