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

函数

全部展开

创建分类树

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

改进分类树

cvloss Classification error by cross validation

解释分类树

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`predictorImportance`	Estimates of predictor importance for classification tree
`shapley`	Shapley values
`surrogateAssociation`	Mean predictive measure of association for surrogate splits in classification tree
`view`	View classification tree

交叉验证

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

测量性能

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

为观测值分类

`predict`	Predict labels using classification tree
`resubPredict`	Predict resubstitution labels of classification tree

类

`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.

View Decision Tree

Create and view a text or graphic description of a trained decision tree.

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.