A decision tree model is a supervised learning algorithm based on a tree structure. It is a graphical tool used for decision analysis, evaluating project risk, and calculating the probability of the net present value being greater than or equal to zero based on known probabilities of various situations. ProcessOn supports online creation of decision tree models and offers a vast number of decision tree model templates and examples that you can copy, allowing you to easily create professional and visually appealing decision tree models.
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The decision tree model represents different decision paths through branch nodes, while leaf nodes represent various possible outcomes or outputs. Each internal node (non-leaf node) represents a test condition or decision point, and based on the different values of this condition, the decision path will lead to different child nodes.
Decision Node (Tree Root): This is the starting point of the decision tree, indicating a decision needs to be made.
Option Branch: Several branches are drawn from the decision node, each representing a possible option or decision path, known as option branches.
Chance Node: Represents uncertain events or unknown outcomes.
Probability Branch: Branches drawn from the state node are called probability branches. The number of probability branches indicates the number of possible natural states, and each branch needs to specify the content of the state and its probability.
Terminal Node (Leaf): Also known as the end node, this is the endpoint of the decision process, reflecting the final state or decision outcome under different conditions.
1. Clearly define the decision objective, ensuring the root node accurately reflects the core issue.
2. Branch design should follow the principles of mutual exclusivity and completeness, with each node corresponding to a specific condition, and branches covering all possible scenarios to avoid omissions or overlaps.
3. Verify logical consistency to ensure that every path from root to leaf complies with actual business rules.
Business Analysis: Customer segmentation and targeted marketing, dividing groups based on characteristics such as customer age and consumption habits to formulate targeted strategies; it can also assist in product pricing, analyzing factors like cost and market demand to determine the optimal price.
Medical Diagnosis: Assists doctors in diagnosing diseases step by step based on patient symptoms and test results, improving diagnostic accuracy and efficiency.
Credit Evaluation: Assessing loan risk levels based on income, debt, credit history, etc., to decide whether to lend.
It is necessary to organize features (such as age, income) and target variables (such as whether to purchase), and handle missing or outlier values to ensure data quality.
Decision trees are suitable for handling numerical data (such as age, income) and categorical data (such as gender, occupation).
Merge low-frequency or similar branches, focus on key decision points, or split complex subtrees into independent modules.
Decision trees make hierarchical decisions through branching rules, suitable for nonlinear relationships; logistic regression directly outputs probabilities, suitable for linearly separable problems. When data is complex, try decision trees first.
Use visual elements such as colors and shapes to distinguish node types (such as decision, opportunity, result) to enhance readability.
