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Machine Learning MCQs - Unit-1
1. Which of the following is not a type of machine learning?
A . Supervised Learning
B . Reinforcement Learning
C . Unsupervised Learning
D . Sequential Programming
2. In supervised learning, the training data consists of
A . Input-output pairs
B . Only input features
C . Only outputs
D . Random samples
3. Which ML type is used for clustering problems?
A . Supervised Learning
B . Reinforcement Learning
C . Unsupervised Learning
D . Deep Learning
4. Reinforcement learning is based on
A . Error correction
B . Rewards and punishments
C . Data clustering
D . Linear regression
5. A neuron receives input through
A . Axon
B . Synapse
C . Dendrites
D . Soma
6. The output of a biological neuron is transmitted through
A . Axon
B . Dendrite
C . Synapse only
D . Myelin
7. The activation function in an artificial neuron mimics
A . Signal strength
B . Neuron firing threshold
C . Memory storage
D . Synaptic pruning
8. The main components of a learning system are
A . Performance element, critic, problem generator, learning element
B . Data, model, algorithm
C . CPU, memory, dataset
D . Input, output, loss function
9. A key issue in machine learning is
A . Bias-variance tradeoff
B . Overfitting
C . Underfitting
D . All of the above
10. Generalization in ML refers to
A . Remembering all training data
B . Fitting only training data
C . Increasing dataset size
D . Performing well on unseen data
11. The task of concept learning is to:
A . Find the simplest hypothesis consistent with data
B . Memorize training data
C . Find the most complex model
D . Minimize memory usage
12. A hypothesis in ML refers to
A . Assumed rule mapping inputs to outputs
B . Random guess
C . Fixed neural weight
D . Dataset
13. A maximally specific hypothesis means
A . Considers all possible generalizations
B . Ignores negative examples
C . Covers only observed positive examples
D . Is the most general hypothesis
14. The version space is defined as
A . All possible hypotheses
B . Set of hypotheses consistent with training examples
C . Hypotheses with minimum error
D . General hypotheses only
15. The Candidate Elimination Algorithm maintains
A . A single hypothesis
B . A boundary of most general and most specific hypotheses
C . Random models
D . Regression equations only
16. The perceptron learning rule updates weights based on
A . Gradient descent
B . Error correction
C . Randomization
D . Clustering
17. Which of the following cannot be learned by a perceptron?
A . AND function
B . OR function
C . XOR function
D . Linearly separable data
18. Data is linearly separable if
A . More than one hyperplane exists
B . Only clusters are possible
C . A straight line can divide classes
D . Regression line can fit
19. The goal of linear regression is to
A . Minimize randomization
B . Maximize number of clusters
C . Classify categorical outputs
D . Minimize squared error between predicted and actual values
20. The line in simple linear regression is represented as
A . y = 1/x
B . y = ax² + bx + c
C . y = log(x)
D . y = mx + c
Fill in the Blanks
21. In supervised learning, the training set contains ____.
22. Unsupervised learning mainly deals with ____ problems.
23. Reinforcement learning uses ____ and ____for feedback.
24. The human brain contains billions of interconnected ____
25. Dendrites in a neuron receive_____
26. The ____ carries output signals away from the neuron.
27. The function that decides whether a neuron fires is called _____
28. The four components of a learning system are performance element, critic, problem generator, and ____
29. The issue of overfitting means the model ____ the training data too well but fails on new data.
30. Generalization in ML refers to good performance on ___ data.
31. The process of concept learning involves searching the ____ space.
32. A maximally specific hypothesis is consistent with only ____ examples.
33. A _____ space contains all hypotheses consistent with training data.
34. The Candidate Elimination Algorithm maintains the most general boundary (G) and the most _____ boundary(S).
35. The perceptron was introduced by_____in 1958.
36. The perceptron cannot solve the ___ problem since it is not linearly separable.
37. A dataset is linearly separable if a _____can separate the classes.
38. Linear regression minimizes the ___ of errors.
39. The equation of a straight line in regression is ___
40. Gradient descent is often used to minimize the _____function in regression.
Relevant Materials :
☞ Machine Learning MCQs - Unit-1 - [ ML ]
☞ Machine Learning MCQs - Unit-2 - [ ML ]
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