Decision Tree 편집하기

'''Decision Tree'''

A '''Decision Tree''' is a supervised learning algorithm used for both classification and regression tasks. It structures decisions as a tree-like model, where each internal node represents a test on a feature, each branch represents an outcome of that test, and each leaf node represents a class label or prediction. Decision Trees are highly interpretable and can work with both categorical and numerical data, making them widely applicable across various fields.
==Key Concepts==
*'''Node Splitting''': The process of dividing data at each node based on a feature value that best separates the classes or reduces prediction error. Popular criteria for splitting include:
**'''Gini Impurity''': Measures the likelihood of an incorrect classification by a randomly chosen element; lower values indicate better splits.
**'''Entropy''': Quantifies data disorder, where a decrease in entropy signifies an increase in information gain.
*'''Recursive Partitioning''': The tree is constructed by repeatedly splitting subsets of data at each node, creating branches until stopping criteria are met.
*'''Pruning''': A technique for trimming the tree by removing nodes that offer minimal contribution to accuracy, which helps in reducing overfitting.
==Common Applications==
Decision Trees are used across industries due to their transparent and straightforward structure:
*'''Healthcare''': Used for clinical decision-making and diagnosis, where interpretability is crucial for understanding factors influencing predictions.
*'''Finance''': Applied in credit scoring, risk analysis, and fraud detection, providing clear decision paths for assessment.
*'''Marketing''': Assists in customer segmentation and identifying factors leading to churn, allowing for targeted marketing strategies.
*'''Manufacturing''': Used in quality control to detect defect patterns and in predictive maintenance to estimate equipment lifespan.
==Strengths==
*'''High Interpretability''': The visual and rule-based nature of Decision Trees makes them easy to understand and communicate, even to non-technical stakeholders.
*'''Minimal Data Preparation''': Unlike many models, Decision Trees do not require feature scaling or normalization, making them compatible with raw datasets.
*'''Versatile with Feature Types''': Can handle both categorical and numerical data directly, offering flexibility in data preparation.
==Limitations==
*'''Prone to Overfitting''': Decision Trees can grow overly complex, capturing noise in the training data, which impacts their ability to generalize.
*'''Instability with Small Variations''': A slight change in data can lead to a completely different tree structure, affecting model consistency.
*'''Bias with Imbalanced Data''': Without adjustment, Decision Trees may favor majority classes, leading to biased predictions in imbalanced datasets.
==Techniques for Improved Performance==
*'''Pruning''': Reduces the tree size by cutting off non-informative branches, helping to prevent overfitting.
*'''Ensemble Methods''': Combining Decision Trees in methods like Random Forests or Gradient Boosting reduces individual tree bias and improves accuracy.
*'''Hyperparameter Tuning''': Adjusting parameters like maximum depth and minimum samples per leaf can help control tree growth and balance performance.
==See Also==
*[[Random Forest]]
*[[Gradient Boosting]]
*[[Support Vector Machine]]
*[[Logistic Regression]]
[[Category:Data Science]]