Decision Tree Pruning explained (Pre-Pruning and Post-Pruning)

Summary

This video explains the concept of decision tree pruning, a technique used to prevent decision trees from overfitting to training data. The speaker first outlines the basic decision tree algorithm and highlights the problem of overfitting, which occurs when trees become too complex and fail to generalize to new data. The video covers two pruning methods: pre-pruning, which limits tree depth or minimum samples, and post-pruning, which trims back an overgrown tree based on validation set performance. Both techniques are illustrated with examples to show how pruning improves model accuracy and generalization.

Takeaways

• 🌳 Pruning is necessary in decision trees to prevent overfitting, which occurs when the tree becomes too complex and fits the training data too closely.
• 📝 The decision tree algorithm works by recursively splitting the data until all partitions are pure, meaning they contain only one class.
• 🚨 A common problem in decision trees is overfitting when there are outliers or unclear class separations, leading to too many layers in the tree.
• 🔄 Pre-pruning prevents the tree from growing too deep by setting constraints, like a minimum number of data points per split or a maximum tree depth.
• 🔢 Pre-pruning example: If the minimum sample size is set to 5, the tree will stop growing once a partition has fewer than 5 data points, even if the data isn't fully pure.
• 📏 Another pre-pruning method is setting a maximum tree depth, which limits the number of layers the tree can have, preventing overfitting.
• ✂️ Post-pruning allows the tree to grow fully and then prunes unnecessary layers by evaluating the performance on a validation set.
• ✅ Post-pruning example: If replacing a decision node with a leaf reduces errors on the validation set, the node is pruned.
• 📊 Post-pruning uses reduced error pruning, which removes nodes that don’t improve predictions, simplifying the tree without reducing accuracy.
• 💡 Combining pre-pruning and post-pruning can result in a well-balanced decision tree that generalizes well on unseen data.

Q & A

Why is pruning necessary in decision trees?

Pruning is necessary to prevent decision trees from overfitting the training data. Overfitting occurs when a tree becomes too complex by adding unnecessary layers to perfectly classify the training data, which reduces its ability to generalize well to unseen data.

What is the general process of a basic decision tree algorithm?

The decision tree algorithm first checks if the data is pure (all examples belong to the same class). If so, it creates a leaf and stops. If the data is not pure, it determines potential splits, selects the best one, and splits the data accordingly. This process is repeated for all partitions of the data until they become pure.

What happens when classes are not clearly separated or when there are outliers in the data?

When classes are not clearly separated or there are outliers, the decision tree may create too many layers by continuing to split the data in an attempt to perfectly separate the classes, leading to overfitting.

How does pre-pruning work in decision trees?

Pre-pruning stops the tree from growing too deep by placing constraints on the tree-building process. One approach is to specify a minimum number of samples required to make a split, and if the number of samples is below that threshold, the node becomes a leaf. Another approach is to set a maximum depth for the tree.

Can you give an example of how pre-pruning works using minimum samples?

For instance, if the minimum number of samples required to make a split is set to 5, and a partition contains fewer than 5 data points, a leaf is created based on the most common class among those points, even if the data is not pure.

What is post-pruning, and how is it different from pre-pruning?

Post-pruning allows the decision tree to grow fully, then prunes it back by removing unnecessary splits after the tree has been created. It is done after the tree-building process, unlike pre-pruning, which limits the tree’s growth during its construction.

How is post-pruning implemented?

Post-pruning starts at the deepest decision node and checks if replacing the node with a leaf would improve or maintain performance on a validation set. If the leaf makes fewer or equal errors compared to the decision node, the node is replaced with the leaf.

What is reduced error pruning in post-pruning?

Reduced error pruning is a specific method in post-pruning where a decision node is replaced by a leaf if the leaf leads to fewer or equal prediction errors on a validation set compared to the node.

How can pruning help with outliers in the data?

Pruning helps by preventing the tree from creating unnecessary layers to classify outliers. By pruning back nodes that only handle outliers, the tree can focus on the general patterns in the data, improving its ability to generalize to new data.

Can a decision tree use both pre-pruning and post-pruning?

Yes, a tree can use both pre-pruning and post-pruning. Pre-pruning limits the tree’s growth during construction, while post-pruning prunes back unnecessary splits after the tree is fully built.