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
-
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?
 

1. What is decision tree regression and how does it work?

Decision tree regression is a machine learning algorithm that uses a tree-like model to make predictions based on a set of input features. It works by recursively splitting the data into smaller and smaller subsets, based on the most significant features, until a final prediction can be made.

2. What is overfitting in decision tree regression?

Overfitting occurs when a decision tree model becomes too complex and starts to fit the training data too closely. This can lead to poor performance on new data and a lack of generalizability. In other words, the model becomes too specific to the training data and cannot accurately predict on new data.

3. How can overfitting be avoided in decision tree regression?

There are a few ways to avoid overfitting in decision tree regression. One approach is to limit the depth of the tree, which prevents the model from becoming too complex. Another method is to use pruning techniques, which remove unnecessary branches from the tree. Additionally, using a larger training dataset can also help prevent overfitting.

4. What is the role of cross-validation in avoiding overfitting in decision tree regression?

Cross-validation is a technique used to evaluate the performance of a model on unseen data. It involves splitting the training data into multiple subsets and using each subset as both a training and testing dataset. This helps to assess the model's performance on data that it has not been trained on, which can help identify and prevent overfitting.

5. Are there any other techniques for avoiding overfitting in decision tree regression?

Yes, there are other techniques that can be used to avoid overfitting in decision tree regression. One approach is to use ensemble methods, such as random forests, which combine multiple decision trees to make more accurate predictions. Another technique is to use regularization, which adds a penalty term to the model's cost function to discourage overfitting.

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