Google Professional Machine Learning Engineer Question 221
Select 2Google Cloud PlatformYou are building a machine learning model for a healthcare application that predicts the likelihood of a patient developing a specific condition. The model needs to provide explanations for its predictions to ensure compliance with regulatory requirements and to gain trust from healthcare professionals. Which modeling technique(s) would be most appropriate given the interpretability requirements?
- A
Decision Trees
- B
Random Forests
- C
Neural Networks
- D
Linear Regression
- E
Gradient Boosting Machines
Show answer and explanation
Correct answers: A, D
Explanation
In scenarios where interpretability is critical, such as healthcare applications with regulatory and trust requirements, models like Decision Trees and Linear Regression are the most appropriate. These models are inherently interpretable, allowing stakeholders to understand how predictions are made. Complex ensemble methods and deep learning models, while powerful, lack the transparency needed for such use cases.
- A. Correct.
Decision Trees are inherently interpretable as they provide a clear path from input features to output predictions. Each decision in the tree can be easily explained, making them suitable for applications requiring high interpretability.
- B. Incorrect.
Random Forests, while based on Decision Trees, are an ensemble method that aggregates multiple trees, making them less interpretable. The complexity of the ensemble reduces the ability to trace individual predictions back to specific inputs.
- C. Incorrect.
Neural Networks are highly complex models with many layers and parameters, making them difficult to interpret. They are better suited for tasks where accuracy is prioritized over interpretability.
- D. Correct.
Linear Regression is a simple and interpretable model where the relationship between features and the target variable is expressed as a linear equation. This makes it easy to explain the impact of each feature on the predictions.
- E. Incorrect.
Gradient Boosting Machines, like Random Forests, rely on ensembles of decision trees but incorporate boosting techniques. This increases model complexity and reduces interpretability, making them unsuitable for highly interpretable requirements.