Matlab implements decision tree algorithm (multiple complete simulation source codes are attached)

Decision trees are a common machine learning algorithm that can be used for both classification and regression problems. In this article, we will introduce how to implement the decision tree algorithm using Matlab.

1. Data preprocessing

Before using the decision tree algorithm, the data needs to be preprocessed, including data cleaning, missing value processing, feature selection, etc. In this article, we use the Iris dataset from the UCI Machine Learning Repository as an example. The Iris dataset contains 3 different iris flowers, each with 4 features: sepal length, sepal width, petal length, and petal width.

First, we need to divide the data set into training set and test set, and convert the data set into a tabular form for subsequent processing.

% 加载数据集
load iris_dataset.mat

% 将数据集转换为表格形式
irisTable = table(meas, species);

% 分割数据集为训练集和测试集
cv = cvpartition(height(irisTable), 'HoldOut', 0.3);
trainData = irisTable(training(cv), :);
testData = irisTable(test(cv), :);

Next, we need to standardize the features so that each feature has a mean of 0 and a standard deviation of 1.

% 对特征进行标准化处理
trainData.meas = zscore(trainData.meas);
testData.meas = zscore(testData.meas);

2. Build a decision tree model

In Matlab, you can use the ClassificationTree function to build a decision tree model. This function can set many parameters, such as maximum tree depth, minimum number of leaf nodes, etc.

% 构建决策树模型
tree = fitctree(trainData, 'species', 'PredictorNames', {
    
    'meas1', 'meas2', 'meas3', 'meas4'}, 'MaxNumSplits', 10);

In the above code, we set the maximum tree depth to 10, that is, the tree has a maximum of 10 layers. We also set the PredictorNames parameter, specifying the names of the features.

3. Test model

After the training is complete, we can use the test set to test the model and calculate the accuracy of the model.

% 使用测试集测试模型
predSpecies = predict(tree, testData(:, 1:4));
accuracy = sum(strcmp(predSpecies, testData.species))/length(testData.species);
fprintf('准确率：%.2f%%\n', accuracy*100);

In the above code, we use the predict function to predict the test set and calculate the accuracy of the model.

4. Visualizing Decision Trees

Matlab provides the view function, which can easily visualize the decision tree model.

% 可视化决策树
view(tree, 'Mode', 'graph');

In the above code, we visualized the decision tree model using the view function.

5. Summary

This article introduces how to use Matlab to implement the decision tree algorithm, and uses the Iris data set as an example for demonstration. Decision trees are a common machine learning algorithm that can be used for both classification and regression problems. In practical applications, we can adjust the parameters of the decision tree algorithm according to the actual situation to obtain better performance.

6. Complete simulation source code download

Implementation of decision tree and random forest algorithm based on Matlab (complete source code + documentation + data).rar: https://download.csdn.net/download/m0_62143653/87959445

Implementation of decision tree C4.5 algorithm based on Matlab (complete source code + documentation + data).rar: https://download.csdn.net/download/m0_62143653/87864281

News data prediction simulation based on Matlab decision tree (complete source code + documentation + data).rar: https://download.csdn.net/download/m0_62143653/87864136

Application research simulation of decision tree classifier in breast cancer diagnosis based on Matlab (complete source code + documentation + data).rar: https://download.csdn.net/download/m0_62143653/87782291