How to use decision trees for classification in Python?

In the field of machine learning, classification is an important task. The decision tree is a commonly used classification algorithm that can divide the data set by repeatedly selecting the best features, making the features within each subset relatively simple and the categories relatively broad. This article will show you how to use decision trees for classification in Python.

1. What is a decision tree?

Decision tree is a tree-structured classification model. The decision tree model has a tree structure, and in classification problems, it represents the classification process. It starts from the root node, tests an attribute, and divides the training set into several subsets based on the attribute. For each subset, continue dividing in the same way until all categories are separated.

2. Classification process of decision tree

The classification process of decision tree is as follows:

1. Select the best segmentation features.
2. Split the data set into two subsets to match the selection results.
3. Recursively process the subsets and repeat steps 1 and 2.
4. Repeat steps 1 to 3 until all data has been classified.

Selecting the best segmentation features requires measuring the segmentation effect. Usually, we use information entropy to measure the effectiveness of segmentation. Information entropy is a concept in statistics that represents the degree of confusion of information. If a data set only contains data of the same category, the data set is the most ordered and its information entropy is the smallest. On the contrary, the more data of different categories contained in a data set, the higher the degree of confusion of the data set and the greater the information entropy.

When selecting the best segmentation features, we calculate the information gain of each feature. Information gain refers to the reduction in information entropy from a parent node to a child node under a given branch condition. The greater the information gain of a feature, the more helpful this feature is in distinguishing different categories of data in the data set.

3. How to use decision trees for classification in Python?

There are many machine learning libraries in Python that can be used to implement decision tree classifiers. This article introduces how to use the Scikit-learn library to implement decision tree classifiers.

The Scikit-learn library is one of the most commonly used machine learning libraries in Python. It provides a wealth of classification, clustering, regression, dimensionality reduction and other algorithms. The Scikit-learn library provides a class called DecisionTreeClassifier that implements a decision tree classifier.

We can use the following code to implement a decision tree classifier based on the Scikit-learn library:

from sklearn.tree import DecisionTreeClassifier

# 将特征和分类目标分别存储到X和y中
X = [[0, 0], [1, 1]]
y = [0, 1]

# 创建决策树并打印结果
clf = DecisionTreeClassifier()
clf = clf.fit(X, y)
print(clf.predict([[2., 2.]]))

Here, we pass the features and classification target as input, create a DecisionTreeClassifier object and perform the train. We can then use this model to feed new data into a classifier to predict its class label.

In addition to using the above syntax, you can also use the following code to complete the classification of the decision tree:

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from IPython.display import Image 
from  pydotplus import graph_from_dot_data

iris = load_iris()
X = iris.data[:, 2:] # 我们只选取花瓣的长度和宽度作为特征
y = iris.target

# 创建决策树并训练
tree_clf = DecisionTreeClassifier(max_depth=2)
tree_clf.fit(X, y)

# 可视化决策树
dot_data = export_graphviz(
    tree_clf,
    out_file=None,
    feature_names=iris.feature_names[2:],
    class_names=iris.target_names,
    rounded=True,
    filled=True
)

graph = graph_from_dot_data(dot_data)
Image(graph.create_png())

In the above code, we use the iris data set as example data and select only Two features are used for classification. We then created a decision tree and trained it.

Finally, we visualize the decision tree using the export_graphviz function, which outputs the decision tree as a .graph file. Next, we draw the decision tree in .png file format using the graph_from_dot_data function. By doing this, we can better understand the execution of the decision tree classifier and the construction of the decision tree.

4. Conclusion

In this article, we introduced the decision tree algorithm and showed how to implement a decision tree classifier using the Scikit-learn library. Decision trees are a commonly used machine learning algorithm that automatically processes input data and feature selection to automatically provide decisions. This algorithm is often used to solve tasks such as classification, prediction, and anomaly detection. Through the examples and code in this article, you can better understand the basic concepts and implementation methods of the decision tree algorithm.

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