How can I implement decision trees in C++?

How can I implement decision trees in C++?

To implement decision trees in C++, you can start by defining a structure for the tree nodes. Each node should contain a feature, a threshold, and pointers to left and right child nodes.

What data structure should I use for the tree nodes?

You can use a struct or a class to define your tree nodes. A simple struct might look like this: struct TreeNode { int feature; double threshold; TreeNode* left; TreeNode* right; };.

How do I train the decision tree with data?

To train the decision tree, you need to recursively split the data based on the best feature and threshold that minimizes impurity, such as Gini impurity or entropy.

Can you give me an example of how to calculate Gini impurity?

Sure! Gini impurity is calculated as: Gini = 1 - sum(p_i^2) where p_i is the proportion of each class in the dataset.