Clustering and Classification Techniques Quiz

1. The confusion matrix in classification contains which four elements?

True positives, false positives, true negatives, false negatives

Precision, recall, accuracy, F1-score

Sensitivity, specificity, positive rate, negative rate

Clusters, classes, features, samples

2. In clustering, what does within-cluster distance measure?

The distance between cluster centers

The distance between points within the same cluster

The distance from a point to the nearest cluster

The total number of clusters formed

3. The F1-score is the harmonic mean of which two metrics?

Accuracy and recall

Precision and accuracy

Precision and recall

Sensitivity and specificity

4. What does overfitting in a classification model mean?

The model performs poorly on training data

The model learns noise and performs poorly on new data

The model uses too few features

The model requires more training samples

5. In logistic regression, the output is a probability between which two values?

-1 and 1

0 and 1

-∞ and ∞

0 and 100

6. The elbow method in clustering helps determine the optimal number of ____.

Features

Clusters

Iterations

Neighbors

7. What is cross-validation used for in classification models?

To increase training data size

To assess model performance and reduce overfitting risk

To normalize feature values

To select the optimal number of clusters

8. In Naive Bayes classification, what assumption is made about features?

All features are equally important

Features are conditionally independent given the class label

Features must be normally distributed

Features are uncorrelated with the target variable

9. Which ensemble method combines multiple decision trees using bootstrap samples?

Gradient Boosting

Random Forest

Bagging

AdaBoost

10. What does the Gini index measure in decision tree construction?

The distance between data points

The purity of a node based on class distribution

The depth of the tree

The number of features used

11. Which clustering algorithm partitions data into k clusters by minimizing within-cluster variance?

K-means

Hierarchical clustering

DBSCAN

Agglomerative clustering

12. In K-nearest neighbors classification, what does the 'k' parameter represent?

The number of clusters

The number of nearest neighbors to consider

The number of features

The number of decision trees

13. What is the main difference between supervised and unsupervised learning?

Supervised uses labeled data; unsupervised does not

Supervised is faster than unsupervised

Unsupervised requires more computational power

Supervised is only for classification tasks

14. Which classification algorithm uses the concept of maximum margin to separate classes?

Naive Bayes

Support Vector Machine

K-Nearest Neighbors

Logistic Regression

15. In clustering evaluation, the silhouette coefficient measures what?

The number of clusters formed

How similar an object is to its cluster compared to other clusters

The distance between cluster centers

The total variance within clusters

16. What does precision measure in a classification model?

The ratio of true positives to all positive predictions

The ratio of true positives to all actual positives

The ratio of true negatives to total samples

The average of true positives and true negatives

17. Which metric measures the proportion of correct predictions in classification?

Precision

Recall

Accuracy

F1-score

18. In a decision tree, what does a leaf node represent?

A feature used for splitting

A final class label or prediction

An intermediate decision point

A rejected branch

19. What is the primary advantage of DBSCAN over K-means?

It requires fewer iterations

It can identify clusters of arbitrary shape and detect outliers

It always converges faster

It works only with numerical data

20. In hierarchical clustering, what does agglomerative clustering do?

Splits clusters into smaller groups

Merges clusters bottom-up from individual points

Assigns points to fixed cluster centers

Removes outliers before clustering

Data+ Clustering and Classification Techniques Quiz