Decision trees are a type of supervised learning algorithm used for both classification and regression tasks.
They are called “decision trees” because they resemble a tree structure, with a root node, internal nodes, and leaf nodes.
XGBoost is an ensemble of decision trees, so understanding how individual trees work is essential.
Structure of a Decision Tree
A decision tree consists of nodes and edges:
- The root node is the starting point of the tree, representing the entire dataset.
- Internal nodes represent a feature or attribute test, and the edges represent the outcome of the test.
- Leaf nodes represent the final prediction or class label.
How Decision Trees Make Predictions
To make a prediction, the tree is traversed from the root node to a leaf node:
- At each internal node, the feature value is tested, and the corresponding edge is followed based on the outcome.
- This process is repeated until a leaf node is reached, which contains the prediction or class label.
Splitting Criteria
At each internal node, the feature and split point are chosen to maximize the information gain or minimize the impurity:
- Common splitting criteria include Gini impurity, entropy, and mean squared error.
- The goal is to create subsets that are as homogeneous as possible with respect to the target variable.
Advantages of Decision Trees
Decision trees offer several advantages:
- Easy to interpret and visualize
- Can handle both categorical and numerical features
- Require minimal data preprocessing
- Perform well on non-linear relationships
Limitations of Decision Trees
Decision trees also have some limitations:
- Prone to overfitting, especially when the tree is deep
- Can be unstable, as small changes in the data can lead to significantly different trees
- May not be the best choice for tasks with a large number of features
Role in XGBoost
XGBoost leverages decision trees in the following ways:
- XGBoost builds an ensemble of decision trees, combining their predictions to make the final prediction.
- Each tree is built sequentially, with the subsequent trees focusing on the errors made by the previous trees.
- This iterative process allows XGBoost to create a powerful and accurate model.