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.
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