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Vecinos más próximos

Clasificación de los k vecinos más próximos mediante búsqueda en un árbol Kd

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

Train models to classify data using supervised machine learning

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
`createns`	Create nearest neighbor searcher object

Interpretar un modelo de los vecinos más próximos

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values

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

Funciones relacionadas

`pdist`	Pairwise distance between pairs of observations
`pdist2`	Pairwise distance between two sets of observations

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

Speaker Identification Using Pitch and MFCC (Audio Toolbox)