Key Storage (Global)
High-quality technical overview of Key Storage in the context of blockchain security.
Components: 1. Root Node. 2. Internal Nodes (Splits). 3. Leaf Nodes (Outcomes). 4. Branches. Algorithms: ID3, C4.5, C5.0, CART (Classification and Regression Trees).
graph LR
Center["Key Storage (Global)"]:::main
Rel_key_management["key-management"]:::related -.-> Center
click Rel_key_management "/terms/key-management"
classDef main fill:#7c3aed,stroke:#8b5cf6,stroke-width:2px,color:white,font-weight:bold,rx:5,ry:5;
classDef pre fill:#0f172a,stroke:#3b82f6,color:#94a3b8,rx:5,ry:5;
classDef child fill:#0f172a,stroke:#10b981,color:#94a3b8,rx:5,ry:5;
classDef related fill:#0f172a,stroke:#8b5cf6,stroke-dasharray: 5 5,color:#94a3b8,rx:5,ry:5;
linkStyle default stroke:#4b5563,stroke-width:2px;
🧒 5살도 이해할 수 있게 설명
Imagine you are playing '20 Questions' with a robot. The robot has a list of rules: 'If the [object](/ko/terms/object) is round, ask if it’s a fruit. If it’s a fruit, ask if it’s red. If it’s red, say Apple.' Every question is a branch on a [tree](/ko/terms/tree). By following the branches, the robot finds the right answer every time. That's a decision tree!
🤓 Expert Deep Dive
Technically, building a decision tree involves 'Recursive Partitioning'. At each node, the algorithm chooses the 'Feature' that best splits the data into the most 'Pure' groups. This is measured using 'Entropy' (Information Gain) or 'Gini Impurity'. If a split significantly reduces entropy, it becomes a branch. To prevent the tree from becoming too complex and 'Overfitting' the training data, we use 'Pruning'—cutting off branches that provide little predictive power. While single trees are easy to interpret, they are often combined into 'Ensembles' like Random Forests or XGBoost, which use hundreds of trees together to achieve state-of-the-art accuracy in tasks like credit scoring or medical diagnosis.