A healthcare company wants to classify patients into risk categories based on their medical history. They have a vast amount of patient data, but the relationships between variables are complex and non-linear. Which algorithm might be more suitable for this task?
- Decision Trees
- K-Nearest Neighbors (K-NN)
- Logistic Regression
- Naive Bayes
Decision Trees are suitable for complex and non-linear relationships between variables. They can capture intricate patterns in patient data, making them effective for risk categorization in healthcare.
Loading...
Related Quiz
- You are working on a dataset with a large number of features. While some of them seem relevant, many appear to be redundant or irrelevant. What technique would you employ to enhance model performance and interpretability?
- What potential problem might arise if you include a vast number of irrelevant features in your machine learning model?
- Why is it crucial for machine learning models, especially in critical applications like healthcare or finance, to be interpretable?
- Which of the following techniques is used to estimate future rewards in reinforcement learning?
- When models are too simple and cannot capture the underlying trend of the data, it's termed as ________.