Understanding Decision Trees in Machine Learning
Decision trees are a popular algorithm for various machine learning tasks, including classification and regression. They are easy to interpret and can handle both numerical and categorical data.
Decision trees work by splitting the data into subsets based on the value of input features. The tree consists of nodes, which test for a particular feature value, and branches, which represent the outcome of the test, leading to either another node or a leaf.
Example of a Decision Tree Structure
To learn more about how decision trees work, consider diving into these further sections: