Decision Tree Regression: Avoiding Overfitting in Training Data

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  • #1
falyusuf
35
3
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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  • #2
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?
 

FAQ: Decision Tree Regression: Avoiding Overfitting in Training Data

What is overfitting in decision tree regression?

Overfitting in decision tree regression occurs when the model learns not only the underlying patterns in the training data but also the noise. This results in a model that performs well on training data but poorly on unseen test data because it has become too tailored to the specific details of the training set.

How can pruning help in avoiding overfitting?

Pruning helps in avoiding overfitting by reducing the complexity of the decision tree. It can be done by either pre-pruning (stopping the tree growth early based on criteria like maximum depth or minimum samples per leaf) or post-pruning (removing branches from a fully grown tree that have little importance). This generalizes the model better to new data.

What role does the maximum depth parameter play in preventing overfitting?

The maximum depth parameter limits the depth of the tree. By setting a maximum depth, you prevent the tree from becoming too complex and capturing noise in the training data. This helps in creating a simpler model that generalizes better to unseen data, thereby avoiding overfitting.

How does cross-validation help in avoiding overfitting in decision tree regression?

Cross-validation helps in avoiding overfitting by splitting the data into multiple subsets and training the model on different combinations of these subsets. This ensures that the model is evaluated on multiple parts of the data, providing a more robust estimate of its performance and helping to tune hyperparameters to avoid overfitting.

What are some common hyperparameters to tune in decision tree regression to avoid overfitting?

Common hyperparameters to tune in decision tree regression to avoid overfitting include maximum depth, minimum samples per leaf, minimum samples per split, and the complexity parameter (alpha). Adjusting these parameters helps in controlling the complexity of the tree, thereby reducing the risk of overfitting.

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