Vecinos más próximos

Clasificación de los k vecinos más próximos

Para entrenar un modelo de los k vecinos más próximos, utilice la app Classification Learner. Para mayor flexibilidad, entrene un modelo de los k vecinos más próximos mediante fitcknn en la interfaz de línea de comandos. Tras el entrenamiento, prediga las etiquetas o calcule las probabilidades a posteriori pasando el modelo y los datos de los predictores a predict.

Apps

Classification Learner

Entrenar modelos para clasificar datos usando machine learning supervisado

Bloques

ClassificationKNN Predict

Classify observations using nearest neighbor classification model (desde R2022b)

Funciones

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Crear un modelo de los vecinos más próximos

fitcknn Fit k-nearest neighbor classifier

Crear un buscador de los vecinos más próximos

`ExhaustiveSearcher`	Create exhaustive nearest neighbor searcher
`KDTreeSearcher`	Create Kd-tree nearest neighbor searcher
`hnswSearcher`	Hierarchical Navigable Small Worlds (HNSW) approximate nearest neighbor search (desde R2024a)
`createns`	Create nearest neighbor searcher object

Interpretar un modelo de los vecinos más próximos

`lime`	Local interpretable model-agnostic explanations (LIME) (desde R2020b)
`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
`shapley`	Shapley values (desde R2021a)

Realizar una validación cruzada

`crossval`	Cross-validate machine learning model
`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

Medir el rendimiento

`loss`	Loss of k-nearest neighbor classifier
`resubLoss`	Resubstitution classification loss
`compareHoldout`	Compare accuracies of two classification models using new data
`edge`	Edge of k-nearest neighbor classifier
`margin`	Margin of k-nearest neighbor classifier
`resubEdge`	Resubstitution classification edge
`resubMargin`	Resubstitution classification margin
`testckfold`	Compare accuracies of two classification models by repeated cross-validation

Clasificar observaciones

`predict`	Predict labels using k-nearest neighbor classification model
`resubPredict`	Classify training data using trained classifier

Recopilar propiedades de un modelo de los vecinos más próximos

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

Funciones relacionadas

`pdist`	Distancia por pares entre pares de observaciones
`pdist2`	Distancia por pares entre dos conjuntos de observaciones

Objetos

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Modelos de clasificación de los vecinos más próximos

`ClassificationKNN`	k-nearest neighbor classification
`ClassificationPartitionedModel`	Cross-validated classification model

Temas

Train Nearest Neighbor Classifiers Using Classification Learner App
Create and compare nearest neighbor classifiers, and export trained models to make predictions for new data.
Visualize Decision Surfaces of Different Classifiers
This example shows how to visualize the decision surface for different classification algorithms.
Supervised Learning Workflow and Algorithms
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
Classification Using Nearest Neighbors
Categorize data points based on their distance to points in a training data set, using a variety of distance metrics.

Información relacionada

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