You notice that a Decision Tree is providing inconsistent results on different runs. How might you investigate and correct the underlying issue, possibly involving entropy, Gini Index, or pruning techniques?
- Analyze the randomness in splitting and apply consistent pruning techniques
- Change to a different algorithm
- Ignore inconsistent results
- Increase tree depth
Inconsistent results may stem from the randomness in splitting the data. Analyzing this aspect and applying consistent pruning techniques can help create more stable, reproducible results. Attention to the splitting criteria, such as entropy or Gini Index, can further refine the model's behavior.
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