C4.5 algorithm for reconstructing decision trees

C4.5 decision tree algorithm is an improved version of the ID3 algorithm, which builds decision trees based on information entropy and information gain. It is widely used in classification and regression problems and is one of the most commonly used algorithms in the fields of machine learning and data mining.

The core idea of ​​the C4.5 algorithm is to maximize information gain by splitting the data set. This algorithm adopts a top-down recursive splitting method, starting from the root node and selecting an optimal feature for splitting based on the existing data set. By calculating the information gain of each feature, the feature with the largest information gain is selected as the splitting feature, and the data set is divided into multiple subsets based on the value of the feature. Each subset corresponds to a subtree, and then the same splitting operation is performed on each subset recursively until all leaf nodes belong to the same category or a predetermined stopping condition is reached. The final decision tree can be used to classify new samples or predict regression. The nodes of the decision tree represent a feature, the edges represent the value of the feature, and the leaf nodes represent the category of the sample or the predicted value. By following the path from the root node to the leaf node of the decision tree, the category to which the sample belongs or the predicted value can be determined based on the characteristic values ​​of the sample. The advantage of the C4.5 algorithm is that it can handle discrete and continuous features, and has good interpretability and understandability. However, the C4.5 algorithm will cause the decision tree to be too complex when there are many feature values, and it is prone to over-fitting problems. In order to solve this problem, pruning and other methods can be used to optimize the decision

The C4.5 algorithm introduces the information gain ratio during feature selection. Compared with the information gain of the ID3 algorithm, it The entropy of the features themselves is taken into account. By dividing the information gain by the feature entropy, the information gain ratio can eliminate the influence of the feature itself and more accurately measure the contribution of the feature to classification. In addition, the C4.5 algorithm also applies a pruning strategy to prevent over-fitting problems from occurring.

The specific steps of the C4.5 algorithm are as follows:

In the C4.5 algorithm, in order to select the optimal features for splitting, use The information gain ratio is used to evaluate the importance of features. The information gain ratio is defined as the information gain divided by the feature entropy, and its calculation formula is GainRatio(D,A)=Gain(D,A)/SplitInformation(D,A). By calculating the information gain ratio of each feature, the feature with the largest value can be selected as the optimal splitting feature. The purpose of this is to take into account the influence of feature entropy to overcome the bias of information gain and thereby better select features for splitting.

Gain(D,A) represents the information gain obtained by using feature A to split data set D, and SplitInformation(D,A) represents using feature A to split data set D. The information required for splitting is the entropy of feature A. The C4.5 algorithm selects the feature with the largest information gain ratio as the split feature of the current node.

2. Divide the data set into multiple subsets based on the values ​​of the selected features. For discrete features, each value corresponds to a subset; for continuous features, the dichotomy or multi-section method can be used to split to obtain multiple subsets.

3. Recursively perform the same splitting operation on each subset until the stopping condition is met. The stopping condition can be reaching a predetermined tree depth, number of leaf nodes, or classification accuracy, etc.

4. Perform pruning operation. The C4.5 algorithm uses the post-pruning method to prune the complete decision tree after obtaining it to remove some useless split nodes, thereby improving the generalization ability of the model.

And the C4.5 algorithm can also handle the problem of missing values. It uses the majority voting method to solve the processing of missing values, that is, the missing values ​​are classified into the category with the most occurrences.

The C4.5 algorithm has the following advantages:

• can handle both discrete and continuous features.
• Use information gain ratio instead of information gain to better handle feature selection problems.
• Adopt post-pruning method to avoid over-fitting.
• Decision trees are easy to interpret and understand and can be used for visualization.

The C4.5 algorithm also has some shortcomings:

• For high-dimensional sparse data, decision trees are prone to overfitting .
• Decision trees are susceptible to noise and outliers.
• The classification effect of decision trees is not necessarily better than other algorithms. It is necessary to choose the appropriate algorithm according to the specific application scenario.

In short, the C4.5 algorithm is a commonly used decision tree algorithm. It uses information entropy and information gain to select the best partition attributes and can handle multiple categories and The missing value problem has high classification accuracy and easy interpretability, and is widely used in the fields of machine learning and data mining.

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