Decision Tree (Decision Tree) is what, an article to see and understand

Definition of a decision tree

Decision Tree (DT) is a tree-shaped predictive model that simulates the human decision-making process, classifying or predicting data through a series of rules. Each internal node represents a feature test, branches correspond to test results, and leaf nodes store the final decision. This algorithm employs a divide-and-conquer strategy to recursively select the optimal features to divide the data, seeking to maximize the purity of the subset. Decision trees can handle both classification tasks (outputting discrete categories) and regression tasks (outputting continuous values). The core advantage is that the model is intuitive and easy to understand, and the decision path can be traced, but there is a risk of overfitting, which needs to be optimized by pruning and other techniques. As a basic algorithm, decision trees are not only an ideal starting point for understanding the principles of machine learning, but also an important part of integrated methods such as random forests and gradient boosting trees.

How Decision Trees Work

• Feature Selection Mechanisms: The decision tree selects the optimal segmentation features at each node, often using information gain, gain rate or Gini impurity as selection criteria. Information gain is based on information theory concepts and measures how much the features enhance the purity of the category. Gini impurity calculates the probability that a randomly sampled sample will be misclassified, with smaller values indicating greater purity. These metrics help the algorithm to identify the features that best distinguish between categories.
• knot splitting process: Once the features are selected, different splits are used depending on the type of feature. Continuous features are usually selected with the best cut-off point, while discrete features are divided by category. The goal of splitting is to divide the data into subsets that are as pure as possible, such that the samples within the same subset have the same category or similar values. This process proceeds recursively until a stopping condition is met.
• Stop condition setting: Common stopping conditions include the number of node samples falling below a threshold, all samples belonging to the same class, no more features available, or the node depth reaching a limit. Properly setting the stopping condition prevents the tree from overgrowing and controls the model complexity. Stopping too early may lead to underfitting, while stopping too late triggers overfitting.
• Leaf node generation: When a node satisfies the stopping condition, the node becomes a leaf node. The leaf nodes in the classification tree use majority voting to determine the category, and the regression tree takes the sample mean as the predicted value. Leaf nodes store the final decision results to form a complete prediction path.
• Predictive path traversal: When predicting a new sample, it starts from the root node and traverses down the corresponding branch according to the feature values until it reaches a leaf node. All the judgment conditions on the path constitute the decision logic, and the leaf node value is the prediction result. This process simulates the human thinking of step-by-step reasoning.

Algorithm for decision tree construction

• ID3 algorithm: The iterative dichotomizer third generation algorithm supports only discrete features and uses information gain as the feature selection criterion. The algorithm constructs the tree recursively from top down without pruning operation, which is prone to overfitting.ID3 algorithm is simple and easy to understand, and lays the foundation for the development of subsequent algorithms.
• C4.5 Algorithm: An improved version of ID3 that handles continuous features and missing values, introducing a gain rate to overcome the preference of information gain for multi-valued features.C4.5 adds a post pruning step to improve model generalization. This algorithm becomes an important milestone in the development of decision trees.
• CART algorithm: Classification and Regression Tree handles both classification and regression tasks, using the Gini index as the classification criterion and variance reduction for regression.CART generates binary trees with only two branches per node. The algorithm includes pruning optimization, which balances model accuracy and simplicity through cost-complexity pruning.
• CHAID algorithm: Cardinality automatic interaction detection is based on statistical significance tests and is suitable for dealing with category-based features. The algorithm performs multiple splits with each branch corresponding to a feature category.CHAID is widely used in marketing and social science research.
• Modern extended algorithms: Includes improved versions of conditional inference trees, multivariate decision trees, and more. Conditional inference trees combine statistical tests with recursive partitioning, and multivariate decision trees allow nodes to use linear combinations of multiple features. These extensions enhance the expressive power of traditional decision trees.

Types of decision trees are distinguished

• Classification trees and regression trees: Classification trees deal with discrete target variables and output category labels; regression trees deal with continuous target variables and output real values. The classification tree is split using purity metrics, and the regression tree is divided based on variance reduction. There is a significant difference between the two in the way leaf nodes make decisions.
• Binary and multinomial trees: CART algorithm generates binary tree, each node produces two branches; ID3, C4.5 algorithm to construct a multinomial tree, the number of branches is related to the number of feature values. The binary tree model has a simple structure, and the multinomial tree is more intuitive but prone to over-segmentation of data.
• Univariate vs. multivariate decision trees: Traditional decision trees are univariate trees where each node is divided based on only one feature; multivariate decision tree nodes use linear combinations of multiple features and can learn more complex decision boundaries. Multivariate trees are more expressive but less explanatory.
• Standard versus regular decision trees: A standard decision tree maintains a tree structure, a rule-based decision tree transforms a path into an if-then rule set. The rule representation is more compact and suitable for knowledge base construction and expert system development.
• Standard and Optimized Trees: Optimization trees apply optimization techniques such as pruning and feature selection to improve generalization performance. The standard tree may overfit the training data, and the optimized tree performs more stably on the test set. The choice of type needs to consider the specific task requirements and data characteristics.

Practical Applications of Decision Trees

• Medical diagnostic systems: Decision tree assists doctors in disease diagnosis, inferring the type of disease through symptoms, examination indicators and other characteristics. The system can integrate medical guidelines and clinical data to provide decision support. For example, breast cancer risk assessment, diabetes diagnosis and other scenarios.
• Financial Credit Scoring: Banks and financial institutions use decision trees to assess customer credit risk, predicting the probability of default based on income, liabilities, historical credit and other characteristics. The model provides a transparent basis for decision-making and meets financial regulatory requirements.
• Customer Relationship Management: Enterprises apply decision trees for customer segmentation and churn prediction to develop personalized marketing strategies for different customer groups. The model analyzes purchase history and demographics to identify high-value customers.
• Industrial Troubleshooting: Manufacturing uses decision trees to analyze equipment sensor data and quickly locate the cause of failures. The interpretability of the tree model helps engineers understand the failure mechanism and make timely maintenance interventions.
• Ecological and environmental research: Ecologists use decision trees to predict species distributions and analyze environmental impact factors. Models handle multidimensional features such as climate, soil, and topography to support biodiversity conservation decisions.

Advantageous features of decision trees

• The model is intuitive and easy to understand: Decision trees simulate the human decision-making process, and the tree structure visualizes the reasoning path. The logic of the model can be understood by non-professionals, a feature that is especially important in scenarios that require model interpretation.
• Requires less data preprocessing: Decision trees deal with mixed-type features with no strict requirements on data distribution and no need for standardization or normalization. The algorithm is robust to missing values and simplifies data preparation.
• Efficient handling of high-dimensional data: The algorithm automatically performs feature selection, ignoring irrelevant features and focusing on important variables. This feature is suitable for processing datasets with a large number of features, such as gene expression data, text feature data.
• Relatively low computational complexity: The time complexity of constructing a decision tree is linearly related to the number of samples and features, and the training efficiency is higher. The prediction stage only needs to traverse the tree path, and the calculation speed is faster.
• Support for multiple output tasks: Decision trees can be extended to multi-output trees by handling multiple target variables simultaneously. This capability is of practical value in scenarios where multiple variables of interest need to be predicted jointly.

Limitations of decision trees

• Prone to overfitting: Decision trees may over-learn noisy and idiosyncratic patterns in the training data, leading to reduced generalization ability. While pruning techniques mitigate this problem, avoiding overfitting completely remains challenging.
• Sensitivity to data fluctuations: Small changes in the training data may lead to the generation of completely different tree structures, and this instability affects model reliability. Integrated learning methods such as random forests can improve this deficiency.
• Ignore inter-feature correlation: The standard decision tree treats each feature independently, ignoring the correlation between features. This limitation affects model performance in datasets where features are highly correlated.
• Difficulty in learning complex relationships: A single decision tree is suitable for learning axis-parallel decision boundaries, and it is difficult to capture complex interactions and nonlinear relationships between features. There are limitations on model representation.
• There is a greedy algorithm flaw: Decision tree construction employs a greedy strategy where each node chooses a locally optimal division that does not guarantee a globally optimal solution. This property may lead to suboptimal tree structures.

Optimization Strategies for Decision Trees

• Application of pruning techniques: Pre-pruning stops growth early in the tree generation process, and post pruning builds the complete tree before pruning branches. Pruning reduces model complexity and improves generalization performance. Cost-complexity pruning is a commonly used post pruning method.
• Feature Selection Optimization: In addition to standard feature selection metrics, statistical tests or regularization methods can be introduced to select a more robust subset of features. Feature selection optimization enhances model resistance to noise.
• Integrated Learning Methods: Combining multiple decision trees into a random forest or gradient boosting tree reduces variance through collective decision making. The integration method significantly improves the prediction accuracy and is the mainstream direction of modern machine learning.
• Data preprocessing enhancements: Resampling techniques are used for unbalanced data and smoothing is used for noisy data. Proper data preprocessing provides higher quality inputs for decision tree learning.
• hyperparameter tuning: Optimize hyperparameters such as maximum depth of the tree, minimum number of leaf node samples, etc. by grid search or random search. Systematic tuning helps to discover the optimal model configuration.

Decision trees in relation to related concepts

• Decision Trees and Rule Learning: Decision trees can be transformed into rule sets where each path corresponds to an if-then rule. Rule learning is more flexible, as the set of rules can be learned directly without going through the intermediate representation of the tree structure.
• Decision Trees and Cluster AnalysisClustering is an unsupervised learning method, and decision tree is supervised learning. However, the decision tree splitting process contains the idea of clustering, the pursuit of subset internal homogeneity, and clustering goals are similar.
• Decision Trees and Neural Networks: Neural networks are black-box models and decision trees are interpretable. The combination of the two produces hybrid models such as neural decision trees, which balance expressive power with explanatory needs.
• Decision Trees and Support Vector Machines: Support vector machines to find maximal interval hyperplanes and decision trees to construct hierarchical decision boundaries. The former is suitable for complex boundaries in high-dimensional spaces, and the latter is more intuitive and easy to understand.
• Decision Trees and Bayesian Methods: Plain Bayes is based on a probabilistic framework and decision trees are based on logical judgment. Bayesian methods are suitable for small data sets, decision trees are more efficient in handling large data sets.

Future Development of Decision Trees

• Automated Machine Learning Integration: Decision trees are being integrated into automated machine learning platforms as fundamental algorithms. Automated feature engineering, model selection and hyperparameter optimization lower the threshold for decision tree applications.
• Explainable Artificial Intelligence Push: Demand for AI interpretability grows, decision trees regain attention for their transparency. Researchers develop more concise and stable variants of decision trees to meet trusted AI requirements.
• Big Data Adaptability Enhancement: Distributed decision tree algorithms are continuously optimized to support efficient training on massive amounts of data. Incremental learning techniques enable decision trees to handle data streams and online learning scenarios.
• Multimodal Learning Extensions: The decision tree framework is extended to handle complex data such as images and text, incorporating deep learning techniques to learn richer feature representations.
• Domain-specific optimization: Develop specialized decision tree algorithms for specific domains such as healthcare, finance, law, etc., incorporating domain knowledge constraints to enhance the practical value in specialized scenarios.