L6

Question 1

What are Parameters in the context of machine learning?

Answer 1

Values learned during training

Question 2

What are Hyperparameters?

Answer 2

Set before training (like learning rate, number of neighbors, or regularization parameter C)

Question 3

What is a Decision function?

Answer 3

Takes a dataset as input and gives a decision as output

Question 4

What is the Loss function?

Answer 4

What you are trying to minimize for a single training example to achieve your objective (e.g. square loss)

Question 5

What is a Cost function?

Answer 5

Average of your loss functions over the entire training set (e.g. mean square error)

Question 6

What is a Training set used for?

Answer 6

Learn model parameters

Question 7

What is a Validation set used for?

Answer 7

Tune hyperparameters

Question 8

What is a Test set used for?

Answer 8

Evaluate final model performance

Question 9

Why do we need SVMs?

Answer 9

To find the best line (or hyperplane) possible with the largest margin between classes

Question 10

What does SVM stand for?

Answer 10

Support Vector Machine

Question 11

What is the main goal of SVM in classification?

Answer 11

To separate classes with the widest possible gap or margin

Question 12

What is the Margin in SVM?

Answer 12

The distance between the decision boundary and the closest data points (support vectors)

Question 13

What are Support Vectors?

Answer 13

The data points closest to the boundary that define the position of the decision boundary

Question 14

What is a Hard Margin SVM?

Answer 14

No errors allowed – aims to find a hyperplane that perfectly separates the classes without any misclassification

Question 15

What is a Soft Margin SVM?

Answer 15

Allows some misclassification or overlap and measures how much an instance is allowed to violate the margin

Question 16

What does hyperparameter C control in Soft Margin SVM?

Answer 16

Trade-off between margin size and classification errors

Question 17

True or False: A large C in SVM means more tolerance to errors.

Answer 17

False

Question 18

What is the decision function for a new data point in SVM?

Answer 18

If result ≥ 1 → positive class; If ≤ -1 → negative class; If in between → uncertain zone (margin)

Question 19

What type of problems do SVMs solve?

Answer 19

Convex quadratic optimization problems with linear constraints

Question 20

What is the kernel trick in SVM?

Answer 20

Projects data into a higher-dimensional space where it becomes linearly separable

Question 21

What is a Linear kernel used for?

Answer 21

Fast, simple, when data is already separable

Question 22

What does the RBF kernel offer?

Answer 22

Very flexible, works on complex data

Question 23

What is the purpose of the Gamma parameter in RBF kernels?

Answer 23

Controls how far a single point’s influence reaches

Question 24

What is One-vs-Rest (OvR) in multiclass classification?

Answer 24

One classifier per class vs all others

Question 25

What is One-vs-One (OvO) in multiclass classification?

Answer 25

One classifier for every pair of classes

Question 26

What is a One-Class SVM (OC-SVM) used for?

Answer 26

Learns from one class, useful for novelty or anomaly detection

Question 27

What are the advantages of Linear SVMs?

Answer 27

Accuracy, works well on smaller cleaner datasets, can be more efficient

Question 28

What are the disadvantages of Linear SVMs?

Answer 28

Not suited to larger datasets, less effective on noisier datasets

Question 29

What is a key advantage of Kernel SVMs?

Answer 29

Allow for complex decision boundaries

Question 30

What is a key disadvantage of Kernel SVMs?

Answer 30

Do not scale well with the number of samples

Question 31

What is the role of preprocessing in SVMs?

Answer 31

Requires careful preprocessing of the data and tuning of the parameters

Question 32

What is the meaning of Support Vectors in SVM?

Answer 32

Closest points to the margin

Question 33

What does Margin refer to in SVM?

Answer 33

Distance between boundary and support vectors

Question 34

What is a Hyperplane in SVM?

Answer 34

Decision boundary

Question 35

What does parameter C control in SVM?

Answer 35

Trade-off between margin size and error

Question 36

What is a Kernel in SVM?

Answer 36

Function that transforms data to higher dimensions

Question 37

What does Gamma control in RBF kernels?

Answer 37

Influence of data points in RBF