Multiple Choice Questions (MCQs)

1. What type of machine learning problem is Credit Card Fraud Detection?

a. Clustering

b. Regression

c. Binary Classification

d. Reinforcement Learning

Answer: c. Binary Classification

The model predicts whether a transaction is genuine or fraudulent.

2. Which algorithm is used in this project?

a. Decision Tree

b. Logistic Regression

c. K-Means Clustering

d. Linear Regression

Answer: b. Logistic Regression

Logistic Regression is commonly used for binary classification tasks.

3. Which column represents the target variable in the dataset?

a. Amount

b. Time

c. V1

d. Class

Answer: d. Class

The Class column indicates whether a transaction is genuine (0) or fraudulent (1).

4. Why is accuracy alone not sufficient for fraud detection?

a. It increases training time.

b. The dataset is highly imbalanced.

c. Logistic Regression cannot calculate accuracy.

d. Accuracy only works for regression models.

Answer: b. The dataset is highly imbalanced.

A model may achieve very high accuracy by predicting every transaction as genuine while failing to detect fraud.

5. Which evaluation metric measures how many actual fraudulent transactions are correctly detected?

a. Accuracy

b. Recall

c. Mean Absolute Error

d. R² Score

Answer: b. Recall

Recall measures the proportion of actual fraudulent transactions that the model successfully identifies.