Leaf 🍀 lifting in Slow Motion

Leaf lifting is a machine learning technique used for decision tree optimization, specifically in the context of boosting algorithms. It aims to reduce the complexity of decision trees by merging adjacent leaf nodes that yield similar predictions. The process involves iteratively combining leaf nodes in a bottom-up manner to create a more compact and efficient tree structure.

The leaf lifting procedure typically starts with an initial decision tree, which can be created using any base learning algorithm, such as CART (Classification and Regression Trees). Each leaf node in the tree represents a specific prediction or outcome.

The leaf lifting process begins by evaluating the similarity between adjacent leaf nodes. Various metrics can be used to measure the similarity, including the similarity of prediction values, impurity measures (such as Gini index or entropy), or statistical tests. If the similarity exceeds a certain threshold or satisfies a predefined condition, the adjacent leaf nodes are considered eligible for merging.

When merging two adjacent leaf nodes, the prediction value of the new merged node is often computed as the weighted average of the original leaf nodes' predictions. The weights can be determined based on various factors, such as the number of instances covered by each leaf node or the impurity of the data in each node.

After merging the leaf nodes, the decision tree structure is updated accordingly. The merged nodes are replaced with a single parent node, which becomes a new internal node in the tree. The parent node then becomes the entry point for subsequent branches, redirecting the decision-making process.

The leaf lifting procedure continues iteratively until no further merging is possible, or until a stopping criterion is met. This criterion can be defined based on factors such as a maximum tree depth, a minimum number of instances per leaf, or a predefined maximum number of leaf nodes.

Leaf lifting offers several benefits in decision tree optimization. By reducing the number of leaf nodes, it reduces the model's complexity and improves interpretability. It can also enhance the efficiency of the model by reducing memory requirements and speeding up prediction time. Moreover, leaf lifting can potentially mitigate overfitting by creating a more generalizable tree structure.

It is worth noting that different variations and extensions of leaf lifting exist, and the exact implementation details may vary depending on the specific boosting algorithm or decision tree framework being used.