Which algorithm would you use when you have a mix of input features (both categorical and continuous) and you need to ensure interpretability of the model?
- Random Forest
- Support Vector Machines (SVM)
- Neural Networks
- Naive Bayes Classifier
Random Forest is a suitable choice for mixed input features when interpretability is important. It combines decision trees and is often used for feature selection and interpretability, making it a good option for such cases.
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