Home>Article>Backend Development> What is the decision tree process of Python artificial intelligence algorithm?
is an algorithm that performs classification or regression by dividing a data set into small, manageable subsets. Each node represents a feature used to divide the data, and each leaf node represents a category or a predicted value. When building a decision tree, the algorithm will select the best features to split the data so that the data in each subset belongs to the same category or has similar features as much as possible. This process will be repeated continuously, similar to recursion in Java, until a stopping condition is reached (for example, the number of leaf nodes reaches a preset value), forming a complete decision tree. It is suitable for handling classification and regression tasks. In the field of artificial intelligence, decision tree is also a classic algorithm with wide applications.
The following is a brief introduction to the decision tree process:
Data preparationSuppose we have a restaurant data set , including attributes such as the customer's gender, whether he smokes, and meal time, as well as information about whether the customer leaves a tip. Our task is to use these attributes to predict whether a customer leaves with a tip.
Data Cleaning and Feature EngineeringFor data cleaning, we need to process missing values, outliers, etc. to ensure the integrity and accuracy of the data. For feature engineering, we need to process the original data and extract the most discriminating features. For example, we can discretize meal times into morning, noon and evening, and convert gender and smoking status into 0/1 values, etc.
Divide the data setWe divide the data set into a training set and a test set, usually using cross-validation.
Building a decision treeWe can use ID3, C4.5, CART and other algorithms to build a decision tree. Here we take the ID3 algorithm as an example. The key is to calculate the information gain. We can calculate the information gain for each attribute, find the attribute with the largest information gain as the split node, and construct the subtree recursively.
Model evaluationWe can use indicators such as accuracy, recall, and F1-score to evaluate the performance of the model.
Model tuningWe can further improve the performance of the model by pruning and adjusting decision tree parameters.
Model ApplicationFinally, we can apply the trained model to new data to make predictions and decisions.
Let’s learn about it through a simple example:
Suppose we have the following data set:
| Feature 1 | Feature 2 | Category |
|---|---|---|
| 1 | 1 | Male |
| 1 | 0 | Male |
| 0 | 1 | Male |
| 0 | 0 | Female |
We can pass Construct the following decision tree to classify it:
If feature 1 = 1, it is classified as male; otherwise (that is, feature 1 = 0), if feature 2 = 1, it is classified as male; otherwise (that is, feature 2 = 0), classified as female.
feature1 = 1 feature2 = 0 # 解析决策树函数 def predict(feature1, feature2): if feature1 == 1: print("男") else: if feature2 == 1: print("男") else: print("女")
In this example, we choose feature 1 as the first split point because it can divide the data set into two subsets containing the same category; then we choose feature 2 as the second Split point because it splits the remaining data set into two subsets containing the same category. Finally, we get a complete decision tree that can classify new data.
Although the decision tree algorithm is easy to understand and implement, various problems and situations need to be fully considered in practical applications:
Over-simulation Combined: In decision tree algorithms, overfitting is a common problem, especially when the amount of training set data is insufficient or the feature values are large, it is easy to cause overfitting. In order to avoid this situation, the decision tree can be optimized by pruning first or pruning later.
Prune first: "Prune" the tree by stopping tree construction in advance. Once stopped, the nodes become leaves. The general processing method is to limit the height and the number of leaf samples.
Post-pruning: After constructing a complete decision tree, replace an inaccurate branch with a leaf and use the node The most frequent class tag in the tree.
Feature selection: Decision tree algorithms usually use methods such as information gain or Gini index to calculate the importance of each feature, and then select the optimal features for partitioning. However, this method cannot guarantee the global optimal features, so it may affect the accuracy of the model.
Processing continuous features: Decision tree algorithms usually discretize continuous features, which may lose some useful information. In order to solve this problem, you can consider using methods such as the dichotomy method to process continuous features.
Missing value processing: In reality, data often have missing values, which brings certain challenges to the decision tree algorithm. Usually, you can fill in missing values, delete missing values, etc.
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