Engineering Decision Tree Regression: Avoiding Overfitting in Training Data

Click For 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
Messages
35
Reaction score
3
Homework Statement
Remove the overfitting in the following example.
Relevant Equations
-
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
 
Physics news on Phys.org
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?
 
Thread 'Why wasn’t gravity included in the potential energy for this problem?'

Thread 'Why wasn’t gravity included in the potential energy for this problem?'

I’m looking at the attached vibration problem. The solution in the manual includes the spring potential energy but does NOT include the gravitational potential energy of the hanging mass. Can someone explain why gravitational potential energy is not included when deriving the equation of motion? I tried asking ChatGPT but kept going in circles and couldn't figure out. Thanks!
View full post »

Similar threads

I Ridge Regression Cross Validation
  • · Replies 4 ·
Replies
4
Views
2K
The Scientist as Hero - or not
  • · Replies 1 ·
Replies
1
Views
3K
Why is the U-2 nicknamed the Dragon Lady?
  • · Replies 4 ·
Replies
4
Views
8K