最近邻

k 最近邻分类

要训练 k 最近邻模型，可以使用分类学习器。为了获得更大的灵活性，可以在命令行界面中使用 fitcknn 训练 k 最近邻模型。训练模型后，可将模型和预测变量数据传递给 predict，以预测标签或估计后验概率。

App

使用有监督的机器学习训练模型以对数据进行分类

Classify observations using nearest neighbor classification model (自 R2022b 起)

fitcknn Fit k-nearest neighbor classifier

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

`lime`	Local interpretable model-agnostic explanations (LIME)
`partialDependence`	Compute partial dependence
`permutationImportance`	Predictor importance by permutation (自 R2024a 起)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`shapley`	Shapley values (自 R2021a 起)

`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

`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

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

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

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.

`pdist`	成对观测值之间的两两距离
`pdist2`	两组观测值之间的两两距离

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