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Related Questions
- What are the primary differences between a neural network and a decision tree in terms of architecture and functionality?
- How do neural networks handle complex, non-linear relationships between input variables, whereas decision trees are limited to linear relationships?
- Can you provide an example of a scenario where a neural network would be more suitable than a decision tree, and vice versa?
- How do decision trees handle missing or noisy data, whereas neural networks may struggle with these types of data?
- What are some common applications of decision trees, and how do they compare to neural networks in terms of interpretability?
- How do neural networks learn and adapt to new data, whereas decision trees rely on pre-defined rules and thresholds?
- Can you explain the concept of overfitting in decision trees and how it compares to neural networks?
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