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A More Robust and Underrated Alternative To Random Forests
Extra does not always mean more.
We know that Decision Trees always overfit.
This is because by default, a decision tree (in sklearn’s implementation, for instance), is allowed to grow until all leaves are pure.
As the model correctly classifies ALL training instances, this leads to:
100% overfitting, and
Random Forest address this by introducing randomness in two ways:
While creating a bootstrapped dataset.
While deciding a node’s split criteria by choosing candidate features randomly.
Yet, the chances of overfitting are still high.
The Extra Trees algorithm is an even more robust alternative to Random Forest.
Extra Trees does not mean more trees.
Instead, it should be written as ExtRa, which means Extra Randomized.
ExtRa Trees are Random Forests with an additional source of randomness.
Here’s how it works:
Create a bootstrapped dataset for each tree (same as RF)
Select candidate features randomly for node splitting (same as RF)
Now, Random Forest calculates the best split threshold for each candidate feature.
But ExtRa Trees chooses this split threshold randomly.
This is the source of extra randomness.
After that, the best candidate feature is selected.
This further reduces the variance of the model.
The effectiveness is evident from the image below:
Decision Trees entirely overfit
Random Forests work better
Extra Trees performs even better
⚠️ A cautionary measure while using ExtRa Trees from Sklearn.
By default, the
bootstrap flag is set to
Make sure you run it with
bootstrap=True, otherwise, it will use the whole dataset for each tree.
👉 Over to you: Can you think of another way to add randomness to Random Forest?
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