COSC 320 Decision Tree
Decision Trees as a Machine Learning Algorithm and Their Application in Poker Strategy *
- A decision tree is a popular machine learning algorithm that models decisions based on input data,
- splitting data at each node according to specific criteria. Each node in the tree represents a
- question or condition about an input feature, with branches corresponding to possible answers.
- By traversing the tree from the root to a leaf node, the algorithm reaches a decision or prediction.
- In machine learning, decision trees are trained on labeled data. For example, in a classification
- task, a decision tree learns to predict categories (like "spam" or "not spam") by finding the best
- questions (features and values) to split the data at each node, optimizing for criteria like
- information gain or Gini impurity. This process allows the tree to generalize patterns in data
- for making accurate predictions on unseen examples. Decision trees are used in both classification
- (discrete outputs) and regression (continuous outputs) tasks and serve as the foundation for
- ensemble methods like Random Forests and Gradient Boosting.
- Application in Poker Strategy:
- In this poker code, a decision tree structure is applied to model poker decisions based on key
- factors like hand strength, pot odds, and the number of players who called the big blind. While
- this decision tree is not "learned" from data in the same way as in machine learning, it uses a
- structured series of decision nodes to simulate how a machine learning model might evaluate a
- poker hand. Each node in the tree represents a choice—whether to "raise," "call," or "fold"—and
- branches are determined by game conditions.
- Although the poker decision tree here is hard coded, it showcases the power of decision trees as
- decision-making frameworks. If extended with a training component, the model could learn optimal
- poker strategies from past game data, transforming it into a machine learning-based decision
- tree.