Your Decision Tree is suffering from high bias. How could adjusting the parameters related to entropy or the Gini Index help in this scenario?
- Add more training data
- Increase tree complexity by fine-tuning split criteria
- Reduce tree complexity by fine-tuning split criteria
- Remove features
High bias often means the model is too simple. Adjusting the parameters related to entropy or the Gini Index to create more complex splits can help capture underlying patterns in the data, thereby reducing bias and potentially improving predictive accuracy.
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