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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 PredictClassify observations using decision tree classifier

函数

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fitctreeFit binary decision tree for multiclass classification
compactCompact tree
pruneProduce sequence of classification subtrees by pruning
cvlossClassification error by cross validation
limeLocal interpretable model-agnostic explanations (LIME)
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predictorImportanceEstimates of predictor importance for classification tree
shapleyShapley values
surrogateAssociationMean predictive measure of association for surrogate splits in classification tree
viewView classification tree
crossvalCross-validated decision tree
kfoldEdgeClassification edge for cross-validated classification model
kfoldLossClassification loss for cross-validated classification model
kfoldfunCross-validate function for classification
kfoldMarginClassification margins for cross-validated classification model
kfoldPredictClassify observations in cross-validated classification model
lossClassification error
resubLossClassification error by resubstitution
compareHoldoutCompare accuracies of two classification models using new data
edgeClassification edge
marginClassification margins
resubEdgeClassification edge by resubstitution
resubMarginClassification margins by resubstitution
testckfoldCompare accuracies of two classification models by repeated cross-validation
predictPredict labels using classification tree
resubPredictPredict resubstitution labels of classification tree

ClassificationTreeBinary decision tree for multiclass classification
CompactClassificationTreeCompact classification tree
ClassificationPartitionedModelCross-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.