Databricks Machine Learning Associate Question 549
Single answerYou are tasked with developing a model to predict whether a customer will churn (leave the service) within the next month. The dataset contains categorical features like 'Subscription Type', 'Customer Region', and 'Support Tickets Raised', as well as numerical features like 'Monthly Usage Hours' and 'Monthly Spend'. Which of the following algorithms is the most appropriate for this scenario?
- A
Linear Regression
- B
Logistic Regression
- C
K-Means Clustering
- D
Random Forest Regressor
Show answer and explanation
Correct answer: B
Explanation
Logistic Regression is the most appropriate choice for this scenario because the problem involves binary classification (predicting whether a customer will churn: Yes or No). The algorithm is designed for such tasks and can handle both categorical and numerical data. Other options, such as Linear Regression, K-Means Clustering, and Random Forest Regressor, are not suitable as they are either for regression tasks or unsupervised learning.
- A. Incorrect.
Linear Regression is used for predicting continuous numerical values, not for classification tasks like churn prediction.
- B. Correct.
Logistic Regression is suitable for binary classification problems like predicting whether a customer will churn (Yes/No). It can handle both numerical and categorical features effectively.
- C. Incorrect.
K-Means Clustering is an unsupervised learning algorithm used for grouping data into clusters, not for supervised classification tasks like churn prediction.
- D. Incorrect.
Random Forest Regressor is used for predicting continuous numerical values, not for binary classification tasks. Instead, a Random Forest Classifier could be used for classification.