Databricks Machine Learning Associate Question 547
Single answerA data scientist is tasked with building a machine learning model to predict whether a customer will churn or not for a subscription-based service. The dataset contains categorical and numerical features, and the target variable is binary (churn: Yes/No). The business requires interpretable results to understand the key drivers of churn. Given these requirements, which algorithm is the most appropriate to use?
- A
Linear Regression
- B
Logistic Regression
- C
Random Forest
- D
K-Means Clustering
Show answer and explanation
Correct answer: B
Explanation
Logistic Regression is the most appropriate algorithm for this scenario because it is designed for binary classification tasks and meets the business requirement for interpretability. It allows the data scientist to understand how each feature contributes to the prediction, which is critical for identifying the key drivers of churn. Other options either do not fit the problem type or fail to meet the interpretability requirement.
- A. Incorrect.
Linear Regression is used for predicting continuous numerical values, not binary classification problems like churn prediction.
- B. Correct.
Logistic Regression is a binary classification algorithm that is interpretable and can provide insights into feature importance, making it suitable for this scenario.
- C. Incorrect.
Random Forest is a powerful machine learning algorithm, but it is less interpretable compared to Logistic Regression, which is a key requirement in this case.
- D. Incorrect.
K-Means Clustering is an unsupervised learning algorithm for grouping data into clusters and is not applicable to this supervised binary classification problem.