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Machine Learning MCQs - Unit-2
1. During the forward pass of an MLP, what is computed first?
A . Gradient of loss function
B . Output of hidden layer
C . Error signal
D . Weight adjustment
2. Backpropagation primarily optimizes:
A . Activation functions
B . Network architecture
C . Weights
D . Learning rate
3. Which function is typically used in the output layer for binary classification?
A . Softmax
B . Linear
C . ReLU
D . Sigmoid
4. Which of the following is a common problem when MLPs are too complex?
A . Underfitting
B . Overfitting
C . Training instability
D . Poor data representation
5. The equivalent of a biological neuron in ANN is:
A . Synapse
B . Weight
C . Node
D . Activation function
6. A Multi-Layer Perceptron (MLP) can learn:
A . Only linearly separable functions
B . Only clustering functions
C . Non-linear decision boundaries
D . Only regression tasks
7. The main idea of backpropagation is to:
A . Adjust weights randomly
B . Propagate error backward to update weights
C . Increase dataset size
D . Reduce neurons
8. The output of an RBF neuron depends mainly on:
A . Distance from the center
B . Random initialization
C . Gradient descent
D . Step size
9. The curse of dimensionality mainly affects:
A . Low-dimensional data
B . High-dimensional data
C . Small datasets
D . Discrete datasets
10. The main objective of SVM is to:
A . Minimize training error
B . Randomly separate data
C . Maximize margin between classes
D . Increase number of features
11. In SVM, support vectors are:
A . Points farthest from decision boundary
B . Points outside hyperplane
C . Points closest to decision boundary
D . Random dataset points
12. MLP stands for:
A . Multiple Learning Process
B . Multi-Layer Perceptron
C . Machine Learning Program
D . Modular Learning Process
13. Number of hidden layers in MLP can be:
A . Zero
B . One or more
C . Fixed
D . None
14. Forward propagation moves from:
A . Output to input
B . Input to output
C . Hidden to input
D . Randomly
15. Backpropagation is mainly used for:
A . Testing
B . Weight update
C . Data collection
D . Clustering
16. Backpropagation uses which rule?
A . Hebbian rule
B . Delta rule
C . Bayesian rule
D . Markov rule
17. Activation function commonly used in MLP is:
A . Step
B . Sigmoid
C . Linear
D . Boolean
18. RBF network hidden neurons typically use:
A . Sigmoid
B . Linear
C . Gaussian
D . Step
19. RBF networks are mainly used for:
A . Clustering
B . Classification
C . Sorting
D . Searching
20. Interpolation is used when data is:
A . Missing
B . Known
C . Continuous
D . Binary
21. Basis functions convert input into:
A . Same space
B . Lower space
C . Feature space
D . Output space
22. SVM is mainly used for:
A . Regression only
B . Classification only
C . Both classification and regression
D . Clustering
23. SVM creates an __________ boundary:
A . Curved
B . Linear
C . Optimal
D . Random
24. Kernel trick avoids:
A . Overfitting
B . Explicit feature mapping
C . Training
D . Classification
25. Common SVM kernel is:
A . Gaussian
B . Sigmoid
C . Polynomial
D . All of the above
26. Gradient descent is mainly used in:
A . K-means
B . Backpropagation
C . PCA
D . Decision Trees
27. Distance metrics lose meaning in:
A . Low-dimensional space
B . High-dimensional space
C . Binary space
D . Linear space
Fill in the Blanks
28. Δw = _________________________
29. Margin is distance between hyperplane and_____________________.
30. Primary building block of ANN is____________________.
31. Axon carries _________________signals.
32. Activation function decides whether neuron__________________.
33. XOR problem is solved using_____________________.
34. RBF stands for_________________________.
35. Most common RBF activation is________________________.
36. SVM maximizes the_____________________.
37. Perceptron updates its___________________.
38. MLP contains one or more _________________layers.
39. The last layer is called ________________ layer.
40. Forward propagation computes the_________________.
41. Backpropagation calculates the_____________________.
42. Backpropagation is based on_____________________.
43. Error flows toward the __________________ layer.
44. MLP uses ________________ learning.
45. Activation functions introduce_________________________.
46. RBF networks use __________________ functions.
47. Curse of dimensionality occurs in ________________ dimensional space.
48. Kernel helps SVM handle ________________ data.
Relevant Materials :
☞ Machine Learning MCQs - Unit-1 - [ ML ]
☞ Machine Learning MCQs - Unit-2 - [ ML ]
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