분류 트리

다중클래스 학습을 위한 이진 결정 트리

분류 트리를 대화형 방식으로 성장시키려면 분류 학습기 앱을 사용하십시오. 더 유연한 접근 방법을 원한다면, 명령줄에서 fitctree를 사용하여 분류 트리를 성장시키십시오. 분류 트리를 성장시킨 후 트리와 새 예측 변수 데이터를 predict에 전달하여 레이블을 예측합니다.

앱

분류 학습기

머신러닝 지도 학습을 사용하여 데이터를 분류하도록 모델 훈련시키기

블록

ClassificationTree Predict

Classify observations using decision tree classifier (R2021a 이후)

함수

모두 확장

분류 트리 만들기

`fitctree`	다중클래스 분류를 위한 이진 결정 트리 피팅
`compact`	Reduce size of machine learning model
`prune`	Produce sequence of classification subtrees by pruning classification tree

분류 트리 개선하기

cvloss Classification error by cross-validation for classification tree model

분류 트리 해석하기

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

분류 트리 교차 검증하기

`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

성능 측정하기

`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

관측값 분류하기

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

분류 트리의 속성 수집하기

gather Gather properties of Statistics and Machine Learning Toolbox object from GPU

객체

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

도움말 항목

Train Decision Trees Using Classification Learner App
Create and compare classification trees, and export trained models to make predictions for new data.
지도 학습 워크플로와 알고리즘
지도 학습의 단계와 비모수적 분류 및 회귀 함수의 특성을 알아봅니다.
결정 트리
결정 트리와 결정 트리를 데이터에 피팅하는 방법을 알아봅니다.
Growing Decision Trees
To grow decision trees, fitctree and fitrtree apply the standard CART algorithm by default to the training data.
결정 트리 보기
훈련된 결정 트리의 텍스트 설명 또는 시각적 설명을 만들고 봅니다.
다양한 분류기의 결정 곡면을 시각화하기
이 예제에서는 다양한 분류 알고리즘에 대한 결정 곡면을 시각화하는 방법을 보여줍니다.
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.