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WorksheetsData Mining Quiz 2
Total questions: 50
Worksheet time: 25mins
Which of the following is not typically an unsupervised learning technique?
K-Means
PCA
DBSCAN
Linear Discriminant Analysis (LDA)
PCA transforms the data to:
A lower-dimensional space with dependent features
A higher-dimensional space
A lower-dimensional space with uncorrelated features
The original feature space with normalized values
A key disadvantage of K-Means is:
It can find non-linear boundaries
It is deterministic
It requires labeled data
It assumes clusters are spherical and equal in size
Which evaluation metric is used for clustering when labels are unavailable?
Accuracy
Adjusted Rand Index
Silhouette Score
ROC-AUC
Which algorithm is best suited for discovering clusters with varying density?
K-Means
PCA
DBSCAN
Hierarchical clustering
Which of the following is true for hierarchical clustering?
It cannot be visualized
It doesn’t require a distance metric
It always produces the same number of clusters
It can be agglomerative or divisive
The curse of dimensionality affects:
Linear regression performance
K-Means efficiency and distance accuracy
Label encoding in supervised models
Ensemble model performance
What does t-SNE do?
Clusters data points
Classifies data
Projects high-dimensional data into 2 or 3 dimensions for visualization
Removes noise from data
In PCA, what determines how many components should be selected?
Number of classes
Explained variance ratio
Number of original features
Training error
Which technique can detect anomalies in data using unsupervised learning?
K-Nearest Neighbors
DBSCAN
Logistic Regression
Random Forest
In association rule mining, what does the "support" of a rule indicate?
The strength of the rule
The proportion of transactions that contain the itemset
The frequency of the consequent
The confidence level of the rule
Which of the following measures is used to evaluate the reliability of a rule?
Support
Confidence
Lift
All of the above
If an itemset has high support but low confidence, it means:
The rule is strong
The rule is rare
The rule occurs frequently but not reliably
The rule is invalid
What does "lift" measure in association rules?
Probability of consequent given antecedent
Ratio of observed support to expected support under independence
Support of the antecedent
Confidence of the rule
In the Apriori algorithm, what property helps reduce the number of candidate itemsets?
Downward closure property
Monotonicity
Lift maximization
Confidence boosting
Which algorithm improves upon Apriori by reducing the number of database scans?
FP-Growth
Eclat
A-PrioriTid
Naïve Bayes
What is a key difference between Apriori and FP-Growth?
Apriori uses a tree structure
FP-Growth uses candidate generation
Apriori is faster on sparse data
FP-Growth avoids candidate generation
Which of the following is not an association rule evaluation metric?
Conviction
Cosine
Entropy
Lift
What is the "confidence" of the rule A → B if support(A ∪ B) = 0.2 and support(A) = 0.4?
0.1
0.5
0.8
0.6
The FP-tree structure is used to:
Compress the transaction database
Replace itemsets with item graphs
Increase the number of candidate itemsets
Sort rules in descending lift
In association rule mining, what is a “frequent itemset”?
An itemset that occurs less than 50% of the time
An itemset with a support above a user-defined threshold
A set with high confidence
A rule with low lift
Which of the following best describes the “downward closure” property?
All subsets of a frequent itemset must also be frequent
All supersets of an infrequent itemset must be infrequent
Both A and B
None of the above
In the rule A → B, which itemset is the antecedent?
A
B
A ∪ B
B ∪ C
The rule A → B is more interesting if:
Lift ≈ 1
Confidence ≈ 0
Support is very low
Lift > 1
Which approach does FP-Growth use to mine frequent patterns?
Bottom-up search
Top-down search
Divide-and-conquer
Breadth-first search
What is the main disadvantage of the Apriori algorithm?
Requires sorting
Generates too many candidate itemsets
Cannot find frequent patterns
Works only for binary data
Conviction is used in association rule mining to measure:
Support strength
Rule reliability if consequent is false
Co-occurrence
Lift ratio
Which of the following is an example of an association rule?
{milk, bread}
milk → bread
Support({milk, bread}) = 0.4
{milk, butter, eggs}
What type of data is typically used in market basket analysis?
Sequential
Time-series
Transactional
Graph-based
Which of the following would indicate an interesting rule?
Low support, low confidence, lift = 1
High support, low lift
High confidence, lift > 1
Confidence = support
Which metric is best used when the classes are imbalanced?
Accuracy
Precision
Recall
F1 Score
What does a high precision score indicate?
Few false positives
Few false negatives
High true positives
Low accuracy
Which metric is calculated as: TP / (TP + FN)?
Precision
Recall
Specificity
Accuracy
What is the formula for accuracy?
TP / (TP + FN)
TP / (TP + FP)
(TP + TN) / (TP + TN + FP + FN)
TN / (TN + FP)
Which of the following is not an evaluation metric?
Confusion Matrix
Precision
Gradient
ROC AUC
What does ROC curve plot?
Precision vs Recall
True Positive Rate vs False Positive Rate
Accuracy vs Loss
Recall vs Specificity
Which metric is used to evaluate the ranking ability of a classifier?
F1 Score
ROC AUC
Accuracy
Precision
What does a confusion matrix help you visualize?
Model complexity
Prediction errors
Training time
Number of epochs
If a model has high recall but low precision, what does it mean?
Many relevant items are missed
Many irrelevant items are classified as relevant
High number of true negatives
Perfect accuracy
Which metric combines both precision and recall?
Accuracy
Specificity
F1 Score
ROC AUC
Specificity is defined as:
TN / (TN + FP)
TP / (TP + FN)
TP / (TP + FP)
FP / (FP + TN)
Which metric is best for minimizing false negatives?
Precision
Specificity
Recall
F1 Score
In a confusion matrix, which value represents false positives?
Top-left
Top-right
Bottom-left
Bottom-right
What does an AUC score of 0.5 indicate?
Excellent model
Random guessing
Perfect recall
High accuracy
Which metric increases if you increase the threshold in a classifier?
Recall
False Positive Rate
Precision
F1 Score
What happens to recall when the decision threshold is lowered?
Increases
Decreases
Remains same
Cannot be determined
Which scenario best suits using the Precision metric?
Detecting cancer cases
Spam email detection
Face recognition with many classes
Weather forecasting
What is the primary weakness of accuracy in imbalanced datasets?
Too complex to calculate
Ignores true positives
Doesn’t distinguish between classes
Misleading when one class dominates
What is the ideal value of the F1 Score?
0
0.5
1
Depends on model
Which metric would you use if false positives are costlier than false negatives?
Recall
Accuracy
Precision
F1 Score
