Engineering Decision Tree Regression: Avoiding Overfitting in Training Data

AI Thread Summary
The discussion focuses on the issue of overfitting in decision tree regression, where the model captures noise from the training data instead of general patterns. A participant attempted to reduce overfitting but is uncertain about the effectiveness of their adjustments. They observed a reduction in outliers from seven to five in their modified graph. The conversation seeks confirmation on the changes made and the parameters adjusted to achieve this outcome. Effective parameter tuning is crucial for improving model performance and avoiding overfitting.
falyusuf
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Homework Statement
Remove the overfitting in the following example.
Relevant Equations
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The decision tree in the following curve is too fine details of the training data and learn from the noise, (overfitting).
overfitting.png

Ref: https://scikit-learn.org/stable/aut...lr-auto-examples-tree-plot-tree-regression-py

I tried to remove the overfitting but not sure about the result, can someone confirm my answer? Here's what I got:
result.png
 
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From your graph, it appears that you trained to 5 outliers at max=5, vs the 7 outliers in the original graph. What parameter did you adjust?
 
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