Databricks Machine Learning Associate Question 550
Single answerA data scientist is working on a binary classification problem to predict whether a customer will churn based on features such as customer tenure, monthly charges, and contract type. The dataset contains 10,000 records with no significant class imbalance. The team wants to use a simple, interpretable model that can provide insights into feature importance. Which algorithm would be the most appropriate for this scenario?
- A
Logistic Regression
- B
Support Vector Machine (SVM)
- C
Random Forest
- D
K-Nearest Neighbors (KNN)
Show answer and explanation
Correct answer: A
Explanation
Logistic Regression is the best choice for this scenario because it is simple, interpretable, and effective for binary classification problems. The dataset is not imbalanced, so Logistic Regression will likely perform well without requiring additional techniques to handle class imbalance. Furthermore, its coefficients provide direct insights into feature importance, aligning with the team's requirement for interpretability.
- A. Correct.
Logistic Regression is a simple and interpretable model that is well-suited for binary classification tasks. It provides coefficients for each feature, which can be used to interpret feature importance.
- B. Incorrect.
Support Vector Machines are powerful for classification but are not as interpretable as Logistic Regression. Additionally, they may not be the best choice when interpretability and simplicity are prioritized.
- C. Incorrect.
Random Forest can handle this task effectively, but it is more complex and less interpretable compared to Logistic Regression. The model's feature importance scores are harder to communicate to non-technical stakeholders.
- D. Incorrect.
K-Nearest Neighbors is not inherently interpretable, as its predictions are based on proximity to training samples rather than feature importance. It is also computationally expensive for large datasets.