AIF-C01 Question 186
Single answerA data scientist at a retail company is tasked with building a machine learning model to predict customer churn. The business requirements emphasize the need to understand the reasons behind each prediction for compliance and decision-making. Which type of model should the data scientist prioritize, and why?
- A
A linear regression model because it provides interpretable coefficients.
- B
A deep neural network because it is highly accurate and can handle large datasets.
- C
A random forest model because it is powerful and can deal with non-linear relationships.
- D
A support vector machine with a radial basis function because it is effective for complex decision boundaries.
Show answer and explanation
Correct answer: A
Explanation
The requirement for explainability and transparency in predictions makes a linear regression model the most suitable choice. Linear regression is inherently explainable, as the coefficients provide a clear and direct understanding of the relationship between each feature and the target variable. Other models like deep neural networks or random forests may offer higher accuracy, but they lack the transparency needed for compliance and decision-making in this scenario.
- A. Correct.
Linear regression is a transparent and explainable model because its coefficients directly indicate the relationship between input features and the target variable, making it suitable for scenarios that require interpretability.
- B. Incorrect.
Deep neural networks are generally not transparent or explainable due to their complex architecture. While they may provide high accuracy, they are not ideal for situations where understanding predictions is critical.
- C. Incorrect.
Random forest models, while robust and capable of handling non-linear relationships, are not inherently explainable. They use an ensemble of decision trees, which makes it challenging to interpret the contribution of each feature.
- D. Incorrect.
Support vector machines with a radial basis function kernel are not transparent or explainable because their decision-making process, especially with non-linear kernels, is challenging to interpret.