DSE - Data Science Supervised Learning: Classification Questions and Answers

Question 1

A data science team is developing a model to predict customer churn (whether a customer will leave or stay).
The dataset is imbalanced, with a significantly smaller number of 'churn' instances.
The business priority is to identify as many customers who are likely to churn as possible, even if it means some non-churning customers are incorrectly flagged.
Which evaluation metric should the team primarily focus on optimizing?

Accepted Answer

Recall (Sensitivity)

Answer

Accuracy

Answer

Precision

Answer

Specificity

Question 2

Which of the following is a key characteristic of classification problems in supervised learning?

Accepted Answer

The target variable is categorical.

Answer

The model predicts a continuous numerical value.

Answer

The training data is unlabeled.

Answer

The model learns to group similar data points together.

Question 3

A team has built a binary classification model and visualized its performance using a confusion matrix. The matrix shows the following: True Positives (TP) = 80, False Positives (FP) = 20, True Negatives (TN) = 450, False Negatives (FN) = 50. What is the precision of this model?

Accepted Answer

0.800

Answer

0.615

Answer

0.937

Answer

0.900

Question 4

Which of the following algorithms is NOT typically used for classification tasks?

Accepted Answer

K-Means Clustering

Answer

Logistic Regression

Answer

Support Vector Machines (SVM)

Answer

Decision Trees

Question 5

In the context of building a classification model, what is the primary purpose of a train/test split?

Accepted Answer

To evaluate the model's performance on unseen data and check for overfitting.

Answer

To increase the size of the dataset by creating new data points.

Answer

To train the model on the entire dataset for maximum accuracy.

Answer

To ensure the features are on a similar scale before model training.

Question 6

A financial institution is building a model to detect fraudulent credit card transactions. A 'False Positive' in this context would mean:

Accepted Answer

A legitimate transaction is incorrectly flagged as fraudulent.

Answer

A fraudulent transaction is correctly identified as fraud.

Answer

A fraudulent transaction is missed and classified as legitimate.

Answer

A legitimate transaction is correctly identified as legitimate.