Entropy is an important concept in the C5 algorithm, especially in building decision trees for data classification. Entropy measures the level of uncertainty at each node of the tree, helping to assess the disorder of the data and decide the best way to divide it into subgroups.
The initial entropy of a node reflects the disorganization of the data at that node. The algorithm’s goal is to reduce the total entropy after classification, meaning to increase the organization of the decision tree. To achieve this, the algorithm uses post-classification entropy (weighted entropy) and information gain to determine the optimal way to partition the data.
Through an example of classifying emails into “spam” and “non-spam” based on the number of words in the subject line, we can see how entropy is applied. If the important variable is the number of words in the subject line, entropy measures the level of uncertainty when classifying data at each node, and the goal is to choose a partition that minimizes entropy the most, thereby creating a well-organized decision tree with high classification performance.
Tác giả Hồ Đức Duy. © Sao chép luôn giữ tác quyền
