分类树

用于多类学习的二叉决策树

要以交互方式生成分类树，可以使用分类学习器。为了获得更大的灵活性，可以在命令行中使用 fitctree 生成分类树。生成分类树后，可以将树和新的预测变量数据传递给 predict，以预测标签。

App

分类学习器

使用有监督的机器学习训练模型以对数据进行分类

模块

ClassificationTree Predict

Classify observations using decision tree classifier (自 R2021a 起)

函数

全部展开

创建分类树

`fitctree`	Fit binary decision tree for multiclass classification
`compact`	Reduce size of classification tree model
`prune`	Produce sequence of classification subtrees by pruning classification tree

改进分类树

cvloss Classification error by cross-validation for classification tree model

解释分类树

`lime`	Local interpretable model-agnostic explanations (LIME) (自 R2020b 起)
`nodeVariableRange`	Retrieve variable range of decision tree node (自 R2020a 起)
`partialDependence`	Compute partial dependence (自 R2020b 起)
`permutationImportance`	Predictor importance by permutation (自 R2024a 起)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`predictorImportance`	Estimates of predictor importance for classification tree
`shapley`	Shapley values (自 R2021a 起)
`surrogateAssociation`	Mean predictive measure of association for surrogate splits in classification tree
`view`	View classification tree

交叉验证分类树

`crossval`	Cross-validate machine learning model
`kfoldEdge`	Classification edge for cross-validated classification model
`kfoldLoss`	Classification loss for cross-validated classification model
`kfoldMargin`	Classification margins for cross-validated classification model
`kfoldPredict`	Classify observations in cross-validated classification model
`kfoldfun`	Cross-validate function for classification

测量性能

`loss`	Classification loss for classification tree model
`resubLoss`	Resubstitution classification loss for classification tree model
`compareHoldout`	Compare accuracies of two classification models using new data
`edge`	Classification edge for classification tree model
`margin`	Classification margins for classification tree model
`resubEdge`	Resubstitution classification edge for classification tree model
`resubMargin`	Resubstitution classification margins for classification tree model
`testckfold`	Compare accuracies of two classification models by repeated cross-validation

为观测值分类

`predict`	Predict labels using classification tree model
`resubPredict`	Classify observations in classification tree by resubstitution

收集分类树的属性

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU (自 R2020b 起)

对象

`ClassificationTree`	Binary decision tree for multiclass classification
`CompactClassificationTree`	Compact classification tree
`ClassificationPartitionedModel`	Cross-validated classification model

主题

Train Decision Trees Using Classification Learner App
Create and compare classification trees, and export trained models to make predictions for new data.
Supervised Learning Workflow and Algorithms
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
Decision Trees
Understand decision trees and how to fit them to data.
Growing Decision Trees
To grow decision trees, fitctree and fitrtree apply the standard CART algorithm by default to the training data.
查看决策树
创建并查看已训练的决策树的文本或图描述。
Visualize Decision Surfaces of Different Classifiers
This example shows how to visualize the decision surface for different classification algorithms.
Splitting Categorical Predictors in Classification Trees
Learn about the heuristic algorithms for optimally splitting categorical variables with many levels while growing decision trees.
Improving Classification Trees and Regression Trees
Tune trees by setting name-value pair arguments in fitctree and fitrtree.
Prediction Using Classification and Regression Trees
Predict class labels or responses using trained classification and regression trees.
Predict Out-of-Sample Responses of Subtrees
Predict responses for new data using a trained regression tree, and then plot the results.
Predict Class Labels Using ClassificationTree Predict Block
Train a classification decision tree model using the Classification Learner app, and then use the ClassificationTree Predict block for label prediction.
Human Activity Recognition Simulink Model for Fixed-Point Deployment
Generate code from a classification Simulink^® model prepared for fixed-point deployment.
Identify Punch and Flex Hand Gestures Using Machine Learning Algorithm on Arduino Hardware (Simulink)
This example shows how to use the Simulink® Support Package for Arduino® Hardware to identify punch and flex hand gestures using a machine learning algorithm.

分类树

App

模块

函数