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Árboles de clasificación

Árboles de decisión binarios para aprendizaje multiclase

Para aumentar un árbol de clasificación de forma interactiva, utilice la app Classification Learner. Para mayor flexibilidad, aumente un árbol de clasificación mediante fitctree en la línea de comandos. Tras aumentar un árbol de clasificación, prediga las etiquetas pasando el árbol y los nuevos datos de los predictores a predict.

Apps

Classification Learner

Entrenar modelos para clasificar datos usando machine learning supervisado

Bloques

ClassificationTree Predict

Classify observations using decision tree classifier (desde R2021a)

Funciones

expandir todo

Crear un árbol de clasificación

`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

Mejorar un árbol de clasificación

cvloss Classification error by cross-validation for classification tree model

Interpretar un árbol de clasificación

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

Árbol de clasificación de validación cruzada

`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

Medir el rendimiento

`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

Clasificar observaciones

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

Recopilar propiedades de un árbol de clasificación

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

Objetos

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

Temas

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.
Árboles de decisión
Comprenda los árboles de decisión y aprenda a ajustarlos a los datos.
Growing Decision Trees
To grow decision trees, fitctree and fitrtree apply the standard CART algorithm by default to the training data.
Ver árbol de decisión
Cree y visualice una descripción gráfica o de texto de un árbol de decisión entrenado.
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.