Here are some multiple-choice questions (MCQs) related to anomaly detection:
1. What is the primary goal of anomaly detection in machine learning?
A) To classify data into predefined categories
B) To identify rare patterns that do not conform to expected behavior
C) To predict future data points
D) To minimize the error in predictions
Answer: B) To identify rare patterns that do not conform to expected behavior
2. Which of the following is NOT a typical application of anomaly detection?
A) Fraud detection
B) Network security monitoring
C) Stock price prediction
D) Image outlier detection
Answer: C) Stock price prediction
3. Which of these methods is commonly used for anomaly detection in high-dimensional data?
A) K-Nearest Neighbors (K-NN)
B) Principal Component Analysis (PCA)
C) Linear Regression
D) Decision Trees
Answer: B) Principal Component Analysis (PCA)
4. In the context of anomaly detection, what is a “point anomaly”?
A) An anomaly that affects a group of data points
B) An anomaly that deviates significantly from the general distribution of the data
C) An anomaly that occurs in a sequence of data points
D) An anomaly detected using clustering methods
Answer: B) An anomaly that deviates significantly from the general distribution of the data
5. Which of the following techniques is unsupervised in nature for anomaly detection?
A) Decision Trees
B) K-Means Clustering
C) Support Vector Machines (SVM)
D) Naive Bayes
Answer: B) K-Means Clustering
6. The Isolation Forest algorithm is primarily used for:
A) Classification problems
B) Anomaly detection in high-dimensional datasets
C) Regression problems
D) Dimensionality reduction
Answer: B) Anomaly detection in high-dimensional datasets
7. In anomaly detection, which of the following metrics is typically used to evaluate the performance of an algorithm?
A) Accuracy
B) Mean Squared Error (MSE)
C) Precision and Recall
D) F1 Score
Answer: C) Precision and Recall
8. Which of the following statements about local outlier factor (LOF) is true?
A) LOF detects outliers by measuring the density of a data point compared to its neighbors
B) LOF is a supervised learning algorithm
C) LOF is not suitable for high-dimensional data
D) LOF requires labeled data for training
Answer: A) LOF detects outliers by measuring the density of a data point compared to its neighbors
9. Which of the following is a common challenge when performing anomaly detection in time-series data?
A) Identifying the data’s correct labeling
B) Detecting anomalies in non-stationary data
C) Choosing the right distance metric for clustering
D) Handling missing data
Answer: B) Detecting anomalies in non-stationary data
10. What is the primary assumption in One-Class SVM when detecting anomalies?
A) Data points are evenly distributed across all classes
B) Data points from the normal class are close to each other and form a tight cluster
C) Anomalies are labeled as separate classes during training
D) The data is always Gaussian-distributed
Answer: B) Data points from the normal class are close to each other and form a tight cluster