基于改进ID3算法的决策树构建

介绍了决策树算法的基本原理，指出在决策树构建的过程中，ID3（Interative Dichotomic Version3）算法按照信息增益最大的原则选取属性，公式比较复杂，相应计算的复杂度也比较高，耗费较多的硬件资源．相对于ID3算法而言，条件概率决策树算法按照条件概率最大的原则选取属性，条件概率能够把属性和类别联系在一起，公式比较简单，有效地降低了计算的复杂性，并且决策树的建立提高了系统的精确度和检测速度．

Decision Tree Arithmetic Based on Conditional Probability

The basic principle of decision tree was introduced. In the process of building a decision tree when using ID3 （Interactive Dichotomic Version 3） algorithm, according to the principle of maximum information gain to select attributes, the formula is more complex, the corresponding complexity is relatively high, and consuming hardware resources. Compared with the ID3 algorithm, the conditional probability decision tree algorithm selects the properties in accordance with the principle of maximum conditional probability. Conditional probability is able to link attributes and categories, the formula is relatively simple and effective to reduce the complexity of the calculation, and the establishment of decision tree improves the accuracy and detection speed of the system.