WorksheetsWorksheet Questions Extraction
Total questions: 126
Worksheet time: 1hrs 3mins
Learning Problems, Perspectives, Concept Learning: A learning task is defined by ⟨T, P, E⟩. If a system improves its performance at playing chess with experience, then P refers to:
Winning strategy
Percentage of games won
Number of board states
Training dataset size
Learning Problems, Perspectives, Concept Learning: Which of the following is NOT a valid perspective of machine learning?
Statistical
Algorithmic
Biological
Deterministic logic
Learning Problems, Perspectives, Concept Learning: A concept is represented as a Boolean function over attribute space. This representation is most commonly used in:
Reinforcement learning
Concept learning
Clustering
Dimensionality reduction
Learning Problems, Perspectives, Concept Learning: In concept learning, noise in training data primarily affects:
Hypothesis representation
Learning rate
Consistency of hypothesis
Target function
Learning Problems, Perspectives, Concept Learning: If the hypothesis space contains the target concept, the learner is said to be:
Complete
Sound
Realizable
Optimal
Version Spaces & Candidate Elimination: The version space represents:
All hypotheses consistent with training data
All possible hypotheses
Only the most general hypothesis
Only the most specific hypothesis
Version Spaces & Candidate Elimination: The S-boundary in Candidate Elimination represents:
Maximally general hypotheses
Minimally general hypotheses
Maximally specific hypotheses
Minimally specific hypotheses
Version Spaces & Candidate Elimination: When a positive example is misclassified by S, the algorithm will:
Remove S
Generalize S minimally
Specialize G
Discard the example
Version Spaces & Candidate Elimination: When a negative example is covered by G, the algorithm will:
Generalize G
Remove S
Specialize G minimally
Expand version space
Version Spaces & Candidate Elimination: Candidate Elimination fails when:
Hypothesis space is finite
Training data contains noise
Data is linearly separable
Version space is non-empty
Inductive Bias: Inductive bias refers to:
Error in data
Prior assumptions made by the learner
Sampling bias
Training bias
Inductive Bias: Without inductive bias, a learner can:
Generalize perfectly
Learn any function
Only memorize training data
Always overfit
Inductive Bias: Occam’s Razor is an example of:
Statistical bias
Computational bias
Inductive bias
Representation bias
Inductive Bias: A learner preferring linear hypotheses over nonlinear ones is an example of:
Search bias
Representation bias
Sample bias
Noise bias
Inductive Bias: Which learning algorithm has no explicit inductive bias?
Decision Trees
KNN
Naïve Bayes
Neural Networks
Decision Tree Learning: Decision tree learning performs a:
Depth-first search
Greedy search
Exhaustive search
Random search
Decision Tree Learning: Information Gain is based on:
Variance
Entropy
Mean squared error
Probability density
Decision Tree Learning: A highly unbalanced dataset may cause decision trees to:
Underfit
Ignore minority class
Increase depth unnecessarily
Fail to converge
Decision Tree Learning: Which measure reduces bias toward multi-valued attributes?
Information Gain
Gini Index
Gain Ratio
Chi-square
Decision Tree Learning: Pruning in decision trees helps to:
Increase training accuracy
Reduce variance
Increase bias
Increase tree depth
Hypothesis Representation: Which representation allows disjunctions but not conjunctions?
Decision trees
Propositional logic
The expressive power of a hypothesis space determines:
Learning speed
Generalization ability
Whether the target can be represented
Training time
A more expressive hypothesis space increases the risk of:
Underfitting
Overfitting
Bias
Data leakage
Which hypothesis representation is most interpretable?
Neural networks
Decision trees
SVM
KNN
Conjunctive hypothesis spaces are limited because they cannot represent:
AND relations
OR relations
Linear boundaries
Boolean logic
Algorithm Design & Heuristic Search: A heuristic is used to:
Guarantee optimality
Reduce search space
Increase hypothesis space
Eliminate noise
Algorithm Design & Heuristic Search: Heuristic search in learning primarily trades off:
Bias and variance
Optimality and efficiency
Accuracy and recall
Precision and speed
Algorithm Design & Heuristic Search: Which search strategy is used in decision tree learning?
Backtracking search
Greedy heuristic search
Uniform-cost search
Hill climbing
Algorithm Design & Heuristic Search: A heuristic that always chooses the locally optimal split may lead to:
Global optimum
Local optimum
Overfitting only
Infinite loop
Search in hypothesis space is exponential mainly due to:
Noise
Dimensionality
Sample size
Overfitting
Scenario & Analytical (GATE-Type): If both S and G converge to a single hypothesis, the learner has:
Failed
Generalized
Learned the target concept
Overfitted
Scenario & Analytical (GATE-Type): A large version space indicates:
High confidence
Insufficient data
Perfect learning
Low hypothesis complexity
Adding irrelevant attributes to a decision tree dataset usually:
Improves accuracy
Reduces tree depth
Increases overfitting risk
Has no effect
Which learning setting is most affected by inductive bias?
Supervised
Unsupervised
Reinforcement
All learning settings
A learner that memorizes all examples suffers from:
High bias
High variance
Underfitting
Low complexity
Candidate Elimination assumes the training data is:
Probabilistic
Noisy
Noise-free
Continuous
Which of the following is a search bias?
Limiting hypothesis space
Preferring shorter trees
Using entropy
Removing noise
A decision tree with depth equal to number of attributes implies:
Underfitting
Balanced learning
Possible overfitting
Optimal learning
If entropy before and after split remains same, information gain is:
1
−1
0
Maximum
Learning problems with continuous attributes often require:
Discretization
Elimination
Encoding
Normalization only
Advanced Reasoning: Which factor primarily controls decision tree complexity?
Learning rate
Depth
Feature scaling
Dataset size
A hypothesis consistent with all examples may still fail due to:
Noise
Overgeneralization
Poor generalization
Low bias
Greedy learning algorithms are preferred because they:
Guarantee optimality
Reduce computational cost
Avoid bias
Remove noise
Which component defines what is learnable?
Training set
Hypothesis space
Learning rate
Loss function
Increasing training examples generally causes version space to:
Expand
Remain same
Shrink
Randomize
Decision trees naturally handle:
Missing values
Only numeric data
Only Boolean data
Only balanced datasets
A heuristic that is admissible ensures:
Faster learning
Optimal solution
No overfitting
Noise tolerance
Which is a limitation of decision tree learning?
Interpretability
Handling non-linear boundaries
High variance
Greedy search
Bias-variance tradeoff mainly affects:
Training accuracy
Model selection
Feature scaling
Data cleaning
A learner that always predicts the majority class demonstrates:
High variance
High bias
Optimal learning
Noise fitting
A learning problem where the output is a continuous real value is best categorized as:
Classification
Clustering
Regression
Reinforcement
If the training accuracy is high but test accuracy is low, the model is likely:
Underfitting
Overfitting
Well-generalized
Noise-free
In concept learning, the target function represents:
Learner hypothesis
Optimal hypothesis
True mapping from instances to labels
Training data distribution
Which factor determines whether a hypothesis is consistent?
Hypothesis size
Dataset size
Agreement with all training examples
Prediction confidence
Learning becomes impossible if:
Hypothesis space is infinite
No inductive bias is present
Training data is large
Learning rate is small
The version space shrinks when:
More hypotheses are added
More training examples are observed
Hypothesis space expands
Noise is added
If the S-boundary becomes empty, it indicates:
Successful learning
No hypothesis fits positive examples
Overgeneralization
Complete version space
If G-boundary becomes empty during learning, it implies:
Overfitting
No hypothesis can explain negative examples
Learning failure
Noise-free data
Candidate Elimination maintains:
Only S
Only G
Both S and G
Only most recent hypothesis
Which operation is applied to S when encountering a negative example?
Generalization
Specialization
No change
Deletion
Bias introduced by restricting hypothesis space is known as:
Search bias
Statistical bias
Representation bias
Sampling bias
A learner that prefers smaller trees has bias toward:
Accuracy
Simplicity
Completeness
Variance
Which of the following best explains generalization?
Fitting all training data
Predicting unseen examples correctly
Minimizing training error
Increasing hypothesis complexity
Bias-variance tradeoff implies:
Lower bias always improves accuracy
Lower variance always improves accuracy
Optimal balance is required
Bias and variance are independent
Increasing model complexity generally:
Increases bias
Decreases variance
Decreases bias and increases variance
Improves generalization always
Which criterion selects the attribute that best separates classes?
Entropy
Information Gain
Probability
Variance
Decision tree learning is an example of:
Lazy learning
Eager learning
Reinforcement learning
Unsupervised learning
A tree with zero depth corresponds to:
Perfect classifier
Random classifier
Single node tree
Overfitted model
Which stopping condition helps avoid overfitting?
All attributes used
Node purity threshold
Maximum depth restriction
All of the above
Which scenario leads to the deepest decision tree?
Few attributes, balanced classes
Many irrelevant attributes
Linearly separable data
Small dataset
A hypothesis space that cannot represent the target function leads to:
Overfitting
Underfitting
High variance
Noise sensitivity
Which representation can model XOR easily?
Linear classifier
Decision tree
Single-layer perceptron
Naïve Bayes
Increasing hypothesis expressiveness affects learning by:
Reducing training error
Increasing search complexity
Increasing overfitting risk
All of the above
Which representation supports recursive partitioning?
Neural networks
Decision trees
Rule-based systems
Linear regression
A hypothesis that predicts the same output for all inputs has:
Zero bias
Zero variance
High bias
High complexity
Heuristic search is preferred because exhaustive search is:
Impossible
Inaccurate
Computationally expensive
Unreliable
Greedy algorithms may fail due to:
High bias
Getting trapped in local optima
Overfitting
Noise
Search space in learning refers to:
Input space
Feature space
Hypothesis space
Output space
A heuristic that underestimates cost is called:
Consistent
Inadmissible
Admissible
Complete
Which factor increases heuristic effectiveness?
Randomness
Domain knowledge
Noise
High dimensionality
If adding training data does not reduce error, the likely cause is:
Overfitting
High bias
Data leakage
Noise
A learning algorithm that updates hypothesis after every example is:
Batch learning
Online learning
Unsupervised learning
Lazy learning
Decision tree pruning mainly addresses:
Bias
Variance
Noise
Data imbalance
If hypothesis space is too small, the learner suffers from:
Overfitting
High variance
Underfitting
Noise sensitivity
Which learning scenario relies heavily on inductive bias?
Learning with few training examples
Learning with abundant labeled data
Pure memorization of training set
Unsupervised clustering
Which component defines how learning proceeds?
Hypothesis space
Training examples
Learning algorithm
Target function
If entropy of parent node is zero, information gain is:
Maximum
Zero
Negative
Undefined
Which technique reduces tree complexity without affecting training data?
Feature scaling
Pruning
Discretization
Sampling
A perfectly consistent hypothesis may still perform poorly due to:
Overgeneralization
High bias
Poor inductive bias
All of the above
A learning algorithm that stores all training data and delays computation is:
Eager learner
Lazy learner
Reinforcement learner
Batch learner
The primary role of heuristics in learning is to:
Improve accuracy
Guide hypothesis search
Remove noise
Increase data
Which property ensures convergence to the target hypothesis?
Consistency
Completeness of hypothesis space
Optimality
Learning rate
Learning is said to be stable if:
Small data changes cause large hypothesis changes
Hypothesis remains unchanged with more data
Prediction is constant
Training error is zero
Which factor most increases hypothesis space size?
Number of examples
Number of attributes
Noise level
Learning rate
Decision trees can simulate which learning model?
Linear classifiers
Boolean functions
Probabilistic models
Clustering
A hypothesis that exactly matches training data but fails on test data is:
Optimal
Consistent
Overfitted
Biased
Which assumption is essential for concept learning?
Infinite data
Finite hypothesis space
Noise-free labels
Linear separability
Which property of learning algorithms ensures feasibility?
Optimality
Completeness
Computational efficiency
Expressiveness
If S and G boundaries overlap but are not identical, it indicates:
No learning
Partial learning
Failure
Overfitting
The goal of concept learning is to:
Minimize training error
Identify target function
Store examples
Eliminate bias
Which of the following best describes learning in machine learning?
Memorizing training examples
Improving performance on a task with experience
Executing predefined rules
Searching a database
A learner that always outputs the same hypothesis regardless of data is said to have:
Zero bias
High bias
High variance
No inductive bias
Which issue arises when the training set is not representative of the test distribution?
Overfitting
Sampling bias
Noise
Underfitting
Concept learning assumes that the target concept is:
Probabilistic
Unknown
Changing over time
Non-deterministic
Which learning scenario assumes labeled training examples?
Unsupervised learning
Reinforcement learning
Supervised learning
Semi-supervised learning
Candidate Elimination guarantees correct learning only when data is:
Large
Noisy
Noise-free
Continuous
The most general hypothesis usually predicts:
All negative
All positive
Random labels
Majority class
Which boundary becomes more specific as learning proceeds?
G
S
Version space
Hypothesis space
Candidate Elimination is best described as:
Probabilistic learning
Incremental learning
Lazy learning
Unsupervised learning
Inductive bias is required because:
Data is noisy
Hypothesis space is infinite
Multiple hypotheses fit the data
Learning algorithms are slow
A bias toward simpler models generally reduces:
Bias
Variance
Training error
Sample size
Which statement about bias is TRUE?
Bias always harms learning
Bias enables generalization
Bias eliminates noise
Bias increases hypothesis space
Underfitting occurs when:
Model is too complex
Model captures noise
Model is too simple
Dataset is large
Generalization error is measured on:
Training data
Validation data
Test data
Labeled data only
Decision tree learning assumes attributes are:
Independent
Relevant
Sufficient to classify
Continuous only
Which of the following increases tree depth most?
Pure nodes
Irrelevant attributes
Pruning
Balanced data
Which measure is used in CART decision trees?
Entropy
Information Gain
Gini Index
Gain Ratio
Decision tree learning fails mainly due to:
High bias
High variance
Low accuracy
Small datasets
Post-pruning is performed to:
Increase training accuracy
Reduce test error
Increase tree size
Improve entropy
Which representation can express any Boolean function?
Conjunctive hypothesis
Linear classifier
Decision tree
Single perceptron
A less expressive hypothesis space leads to:
Overfitting
Underfitting
High variance
Noise sensitivity
Which representation favors interpretability over accuracy?
Neural networks
Decision trees
SVM
Ensemble methods
Hypothesis representation directly affects:
Data collection
Learnability
Label noise
Dataset size
Restricting hypothesis space is an example of:
Search bias
Sampling bias
Representation bias
Measurement bias
Which search method guarantees optimality but is impractical?
Greedy search
Heuristic search
Exhaustive search
Random search
Heuristic search algorithms aim to:
Explore entire space
Reduce computational cost
Eliminate bias
Maximize variance
