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Statistics and Machine Learning Toolbox 函数 - 按字母顺序排列的列表

A

`addedvarplot`	Create added variable plot using input data
`addInteractions`	Add interaction terms to univariate generalized additive model (GAM) (自 R2021a 起)
`addK`	Evaluate additional numbers of clusters
`addlevels`	(Not Recommended) Add levels to nominal or ordinal arrays
`addMetrics`	Compute additional classification performance metrics (自 R2022a 起)
`addTerms`	Add terms to linear regression model
`addTerms`	Add terms to generalized linear regression model
`adtest`	Anderson-Darling test
`andrewsplot`	安德鲁斯图
`anova`	Analysis of variance (ANOVA) results (自 R2022b 起)
`anova`	Analysis of variance for between-subject effects in a repeated measures model
`anova`	Analysis of variance for linear regression model
`anova`	Analysis of variance for linear mixed-effects model
`anova`	Analysis of variance for generalized linear mixed-effects model
`anova1`	One-way analysis of variance
`anova2`	Two-way analysis of variance
`anovan`	N-way analysis of variance
`ansaribradley`	Ansari-Bradley test
`aoctool`	Interactive analysis of covariance
`append`	Append new trees to ensemble
`average`	Compute performance metrics for average receiver operating characteristic (ROC) curve in multiclass problem (自 R2022a 起)

B

`barttest`	Bartlett’s test
`barttest`	Bartlett's test for multivariate analysis of variance (MANOVA) (自 R2023b 起)
`BayesianOptimization`	Bayesian optimization results
`bayesopt`	Select optimal machine learning hyperparameters using Bayesian optimization
`bbdesign`	Box-Behnken design
`bestPoint`	Best point in a Bayesian optimization according to a criterion
`betacdf`	beta 累积分布函数
`BetaDistribution`	beta 概率分布对象
`betafit`	Beta parameter estimates
`betainv`	Beta inverse cumulative distribution function
`betalike`	Beta negative log-likelihood
`betapdf`	beta 概率密度函数
`betarnd`	beta 随机数
`betastat`	Beta mean and variance
`binocdf`	二项累积分布函数
`binofit`	Binomial parameter estimates
`binoinv`	Binomial inverse cumulative distribution function
`BinomialDistribution`	二项概率分布对象
`binopdf`	二项概率密度函数
`binornd`	来自二项分布的随机数
`binostat`	Binomial mean and variance
`binScatterPlot`	Scatter plot of bins for tall arrays
`biplot`	Biplot
`BirnbaumSaundersDistribution`	Birnbaum-Saunders probability distribution object
`bootci`	Bootstrap confidence interval
`bootstrp`	Bootstrap sampling
`boundary`	Piecewise distribution boundaries
`boxchart`	Box chart (box plot) for analysis of variance (ANOVA) (自 R2022b 起)
`boxchart`	Box chart (box plot) for multivariate analysis of variance (MANOVA) (自 R2023b 起)
`boxchart`	Visualize Shapley values using box charts (box plots) (自 R2024a 起)
`boxplot`	用箱线图可视化摘要统计量
`BurrDistribution`	伯尔概率分布对象

C

`CalinskiHarabaszEvaluation`	Calinski-Harabasz criterion clustering evaluation object
`candexch`	D-optimal design from candidate set using row exchanges
`candgen`	Candidate set generation
`canoncorr`	Canonical correlation
`canonvars`	Canonical variables (自 R2023b 起)
`capability`	Process capability indices
`capaplot`	Process capability plot
`caseread`	从文件中读取个案名称
`casewrite`	Write case names to file
`cat`	(Not Recommended) Concatenate dataset arrays
`ccdesign`	Central composite design
`cdf`	累积分布函数
`cdf`	高斯混合分布的累积分布函数
`cdfplot`	经验累积分布函数 (cdf) 图
`cell2dataset`	(Not Recommended) Convert cell array to dataset array
`cellstr`	(Not Recommended) Create cell array of character vectors from dataset array
`chi2cdf`	卡方累积分布函数
`chi2gof`	Chi-square goodness-of-fit test
`chi2inv`	卡方逆累积分布函数
`chi2pdf`	卡方概率密度函数
`chi2rnd`	卡方随机数
`chi2stat`	卡方均值和方差
`cholcov`	Cholesky-like covariance decomposition
`ClassificationBaggedEnsemble`	Classification ensemble grown by resampling
`ClassificationDiscriminant`	Discriminant analysis classification
`ClassificationECOC`	Multiclass model for support vector machines (SVMs) and other classifiers
`ClassificationECOCCoderConfigurer`	Coder configurer for multiclass model using binary learners
`ClassificationEnsemble`	Ensemble classifier
`ClassificationGAM`	Generalized additive model (GAM) for binary classification (自 R2021a 起)
`ClassificationKernel`	Gaussian kernel classification model using random feature expansion
`ClassificationKNN`	k-nearest neighbor classification
`ClassificationLinear`	Linear model for binary classification of high-dimensional data
`ClassificationLinearCoderConfigurer`	Coder configurer for linear binary classification of high-dimensional data (自 R2019b 起)
`ClassificationNaiveBayes`	Naive Bayes classification for multiclass classification
`ClassificationNeuralNetwork`	Neural network model for classification (自 R2021a 起)
`ClassificationPartitionedECOC`	Cross-validated multiclass ECOC model for support vector machines (SVMs) and other classifiers
`ClassificationPartitionedEnsemble`	Cross-validated classification ensemble
`ClassificationPartitionedGAM`	Cross-validated generalized additive model (GAM) for classification (自 R2021a 起)
`ClassificationPartitionedKernel`	Cross-validated, binary kernel classification model
`ClassificationPartitionedKernelECOC`	Cross-validated kernel error-correcting output codes (ECOC) model for multiclass classification
`ClassificationPartitionedLinear`	Cross-validated linear model for binary classification of high-dimensional data
`ClassificationPartitionedLinearECOC`	Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data
`ClassificationPartitionedModel`	Cross-validated classification model
`ClassificationSVM`	Support vector machine (SVM) for one-class and binary classification
`ClassificationSVMCoderConfigurer`	Coder configurer for support vector machine (SVM) for one-class and binary classification
`ClassificationTree`	Binary decision tree for multiclass classification
`ClassificationTreeCoderConfigurer`	Coder configurer of binary decision tree model for multiclass classification (自 R2019b 起)
`classify`	Classify observations using discriminant analysis
`cluster`	Construct clusters from Gaussian mixture distribution
`cluster`	Construct agglomerative clusters from linkages
`clusterdata`	Construct agglomerative clusters from data
`cmdscale`	Classical multidimensional scaling
`coefCI`	Confidence intervals of coefficient estimates of linear regression model
`coefCI`	Confidence intervals for coefficients of linear mixed-effects model
`coefCI`	Confidence intervals of coefficient estimates of generalized linear regression model
`coefCI`	Confidence intervals for coefficient estimates of multinomial regression model (自 R2023a 起)
`coefCI`	Confidence intervals for coefficients of generalized linear mixed-effects model
`coefCI`	Confidence intervals of coefficient estimates of nonlinear regression model
`coefci`	Confidence interval for Cox proportional hazards model coefficients (自 R2021a 起)
`coeftest`	Linear hypothesis test on coefficients of repeated measures model
`coeftest`	Linear hypothesis test on MANOVA model coefficients (自 R2023b 起)
`coefTest`	Linear hypothesis test on linear regression model coefficients
`coefTest`	Hypothesis test on fixed and random effects of linear mixed-effects model
`coefTest`	Linear hypothesis test on generalized linear regression model coefficients
`coefTest`	Linear hypothesis test on multinomial regression model coefficients (自 R2023a 起)
`coefTest`	Hypothesis test on fixed and random effects of generalized linear mixed-effects model
`coefTest`	Linear hypothesis test on nonlinear regression model coefficients
`combine`	Combine two ensembles
`combnk`	(Not recommended) Enumeration of combinations
`compact`	Compact clustering evaluation object
`compact`	Compact linear regression model
`compact`	Compact generalized linear regression model
`compact`	Compact support vector machine regression model
`compact`	Reduce size of machine learning model
`compact`	Reduce size of regression tree model
`compact`	Reduce size of regression ensemble model
`compact`	Compact ensemble of decision trees
`compact`	Reduce size of direct forecasting model (自 R2023b 起)
`compact`	Reduce size of classification tree model
`compact`	Reduce size of discriminant analysis classifier
`compact`	Reduce size of multiclass error-correcting output codes (ECOC) model
`compact`	Reduce size of classification ensemble model
`CompactClassificationDiscriminant`	Compact discriminant analysis classification
`CompactClassificationECOC`	Compact multiclass model for support vector machines (SVMs) and other classifiers
`CompactClassificationEnsemble`	Compact classification ensemble
`CompactClassificationGAM`	Compact generalized additive model (GAM) for binary classification (自 R2021a 起)
`CompactClassificationNaiveBayes`	Compact naive Bayes classifier for multiclass classification
`CompactClassificationNeuralNetwork`	Compact neural network model for classification (自 R2021a 起)
`CompactClassificationSVM`	Compact support vector machine (SVM) for one-class and binary classification
`CompactClassificationTree`	Compact classification tree
`CompactDirectForecaster`	Compact direct forecasting model (自 R2023b 起)
`CompactGeneralizedLinearModel`	Compact generalized linear regression model class
`CompactLinearModel`	Compact linear regression model
`CompactRegressionEnsemble`	Compact regression ensemble
`CompactRegressionGAM`	Compact generalized additive model (GAM) for regression (自 R2021a 起)
`CompactRegressionGP`	Compact Gaussian process regression model class
`CompactRegressionNeuralNetwork`	Compact neural network model for regression (自 R2021a 起)
`CompactRegressionSVM`	Compact support vector machine regression model
`CompactRegressionTree`	Compact regression tree
`CompactTreeBagger`	Compact ensemble of bagged decision trees
`compare`	Compare linear mixed-effects models
`compare`	Compare generalized linear mixed-effects models
`compareHoldout`	Compare accuracies of two classification models using new data
`confusionchart`	Create confusion matrix chart for classification problem
`confusionmat`	Compute confusion matrix for classification problem
`controlchart`	Shewhart control charts
`controlrules`	Western Electric and Nelson control rules
`cophenet`	Cophenetic correlation coefficient
`copulacdf`	Copula cumulative distribution function
`copulafit`	Fit copula to data
`copulaparam`	Copula parameters as function of rank correlation
`copulapdf`	Copula probability density function
`copularnd`	Copula random numbers
`copulastat`	Copula rank correlation
`cordexch`	Coordinate exchange
`corr`	线性或秩相关性
`corrcov`	Convert covariance matrix to correlation matrix
`covarianceParameters`	Extract covariance parameters of linear mixed-effects model
`covarianceParameters`	Extract covariance parameters of generalized linear mixed-effects model
`CoxModel`	Cox proportional hazards model (自 R2021a 起)
`coxphfit`	Cox proportional hazards regression
`createns`	Create nearest neighbor searcher object
`crosstab`	Cross-tabulation
`crossval`	Estimate loss using cross-validation
`crossval`	Cross-validate machine learning model
`crossval`	Cross-validate direct forecasting model (自 R2023b 起)
`cvloss`	Regression error by cross-validation for regression tree model
`cvloss`	Loss for partitioned data at each horizon step (自 R2023b 起)
`cvloss`	Classification error by cross-validation for classification tree model
`cvpartition`	Partition data for cross-validation
`cvpredict`	Predict response using cross-validated direct forecasting model (自 R2023b 起)
`cvshrink`	Cross-validate pruning and regularization of regression ensemble
`cvshrink`	Cross-validate regularization of linear discriminant

D

`datasample`	Randomly sample from data, with or without replacement
`dataset`	(Not Recommended) Arrays for statistical data
`dataset2cell`	(Not Recommended) Convert dataset array to cell array
`dataset2struct`	(Not Recommended) Convert dataset array to structure
`dataset2table`	Convert dataset array to table
`datasetfun`	(Not Recommended) Apply function to dataset array variables
`daugment`	D-optimal augmentation
`DaviesBouldinEvaluation`	Davies-Bouldin criterion clustering evaluation object
`dbscan`	Density-based spatial clustering of applications with noise (DBSCAN)
`dcovary`	D-optimal design with fixed covariates
`dendrogram`	Dendrogram plot
`describe`	Describe generated features (自 R2021a 起)
`designecoc`	Coding matrix for reducing error-correcting output code to binary
`designMatrix`	Fixed- and random-effects design matrices
`designMatrix`	Fixed- and random-effects design matrices
`detectdrift`	Detect drifts between baseline and target data using permutation testing (自 R2022a 起)
`detectdrift`	Update drift detector states and drift status with new data (自 R2022a 起)
`devianceTest`	Analysis of deviance for generalized linear regression model
`diagnostics`	Markov Chain Monte Carlo diagnostics
`DirectForecaster`	Fit direct forecasting model (自 R2023b 起)
`discardResiduals`	Remove residuals from Cox model (自 R2022b 起)
`discardSupportVectors`	Discard support vectors for linear support vector machine (SVM) regression model
`discardSupportVectors`	Discard support vectors for linear support vector machine (SVM) classifier
`discardSupportVectors`	Discard support vectors of linear SVM binary learners in ECOC model
`disp`	(Not Recommended) Display dataset array
`disparateImpactRemover`	Remove disparate impact of sensitive attribute (自 R2022b 起)
`display`	(Not Recommended) Display dataset array
`distributionFitter`	打开分布拟合器
`disttool`	Interactive density and distribution plots
`double`	(Not Recommended) Convert dataset variables to double array
`drawSamples`	Generate Markov chain using Hamiltonian Monte Carlo (HMC)
`DriftDetectionMethod`	Incremental drift detector that utilizes Drift Detection Method (DDM) (自 R2022a 起)
`DriftDiagnostics`	Diagnostics information for batch drift detection (自 R2022a 起)
`droplevels`	(Not Recommended) Drop levels from a nominal or ordinal array
`dummyvar`	Create dummy variables
`dwtest`	Durbin-Watson test with residual inputs
`dwtest`	Durbin-Watson test with linear regression model object

E

`ecdf`	Empirical cumulative distribution function
`ecdf`	Compute empirical cumulative distribution function (ecdf) for baseline and target data specified for data drift detection (自 R2022a 起)
`ecdfhist`	Histogram based on empirical cumulative distribution function
`edge`	Classification edge for Gaussian kernel classification model
`edge`	Classification edge for classification tree model
`edge`	Classification edge for discriminant analysis classifier
`edge`	Classification edge for naive Bayes classifier
`edge`	Edge of k-nearest neighbor classifier
`edge`	Find classification edge for support vector machine (SVM) classifier
`edge`	Classification edge for multiclass error-correcting output codes (ECOC) model
`edge`	Classification edge for linear classification models
`edge`	Classification edge for classification ensemble model
`edge`	Classification edge for generalized additive model (GAM) (自 R2021a 起)
`edge`	Classification edge for neural network classifier (自 R2021a 起)
`end`	(Not Recommended) Last index in indexing expression for dataset array
`epsilon`	Epsilon adjustment for repeated measures anova
`error`	Error (misclassification probability or MSE)
`error`	Error (misclassification probability or MSE)
`estimateMAP`	Estimate maximum of log probability density
`evalclusters`	Evaluate clustering solutions
`evcdf`	Extreme value cumulative distribution function
`evfit`	Extreme value parameter estimates
`evinv`	Extreme value inverse cumulative distribution function
`evlike`	Extreme value negative log-likelihood
`evpdf`	Extreme value probability density function
`evrnd`	Extreme value random numbers
`evstat`	Extreme value mean and variance
`ExhaustiveSearcher`	Create exhaustive nearest neighbor searcher
`expcdf`	Exponential cumulative distribution function
`expfit`	Exponential parameter estimates
`expinv`	Exponential inverse cumulative distribution function
`explike`	Exponential negative log-likelihood
`ExponentialDistribution`	指数概率分布对象
`export`	(Not Recommended) Write dataset array to file
`exppdf`	Exponential probability density function
`exprnd`	指数随机数
`expstat`	Exponential mean and variance
`ExtremeValueDistribution`	极值概率分布对象

F

`factoran`	Factor analysis
`fairnessMetrics`	Bias and group metrics for a data set or classification model (自 R2022b 起)
`fairnessThresholder`	Optimize classification threshold to include fairness (自 R2023a 起)
`fairnessWeights`	Reweight observations for fairness in binary classification (自 R2022b 起)
`fcdf`	F 累积分布函数
`FeatureSelectionNCAClassification`	Feature selection for classification using neighborhood component analysis (NCA)
`FeatureSelectionNCARegression`	Feature selection for regression using neighborhood component analysis (NCA)
`FeatureTransformer`	Generated feature transformations (自 R2021a 起)
`feval`	Predict responses of linear regression model using one input for each predictor
`feval`	Predict responses of generalized linear regression model using one input for each predictor
`feval`	Predict responses of multinomial regression model using one input for each predictor (自 R2023a 起)
`feval`	Evaluate nonlinear regression model prediction
`ff2n`	二水平完全析因设计
`fillprox`	Proximity matrix for training data
`finv`	F 逆累积分布函数
`fishertest`	Fisher’s exact test
`fit`	Train robust random cut forest model for incremental anomaly detection (自 R2023b 起)
`fit`	Train one-class SVM model for incremental anomaly detection (自 R2023b 起)
`fit`	Train drift-aware learner for incremental learning with new data (自 R2022b 起)
`fit`	Train kernel model for incremental learning (自 R2022a 起)
`fit`	Train linear model for incremental learning (自 R2020b 起)
`fit`	Fit simple model of local interpretable model-agnostic explanations (LIME) (自 R2020b 起)
`fit`	Compute Shapley values for query points (自 R2021a 起)
`fit`	Train ECOC classification model for incremental learning (自 R2022a 起)
`fit`	Train naive Bayes classification model for incremental learning (自 R2021a 起)
`fitcauto`	Automatically select classification model with optimized hyperparameters (自 R2020a 起)
`fitcdiscr`	Fit discriminant analysis classifier
`fitcecoc`	Fit multiclass models for support vector machines or other classifiers
`fitcensemble`	Fit ensemble of learners for classification
`fitcgam`	Fit generalized additive model (GAM) for binary classification (自 R2021a 起)
`fitckernel`	Fit binary Gaussian kernel classifier using random feature expansion
`fitcknn`	Fit k-nearest neighbor classifier
`fitclinear`	Fit binary linear classifier to high-dimensional data
`fitcnb`	Train multiclass naive Bayes model
`fitcnet`	Train neural network classification model (自 R2021a 起)
`fitcox`	Create Cox proportional hazards model (自 R2021a 起)
`fitcsvm`	训练用于一类和二类分类的支持向量机 (SVM) 分类器
`fitctree`	Fit binary decision tree for multiclass classification
`fitdist`	对数据进行概率分布对象拟合
`fitensemble`	Fit ensemble of learners for classification and regression
`fitglm`	Create generalized linear regression model
`fitglme`	Fit generalized linear mixed-effects model
`fitgmdist`	Fit Gaussian mixture model to data
`fitlm`	拟合线性回归模型
`fitlme`	Fit linear mixed-effects model
`fitlmematrix`	Fit linear mixed-effects model
`fitmnr`	Fit multinomial regression model (自 R2023a 起)
`fitnlm`	Fit nonlinear regression model
`fitPosterior`	Fit posterior probabilities for compact support vector machine (SVM) classifier
`fitPosterior`	Fit posterior probabilities for support vector machine (SVM) classifier
`fitrauto`	Automatically select regression model with optimized hyperparameters (自 R2020b 起)
`fitrensemble`	Fit ensemble of learners for regression
`fitrgam`	Fit generalized additive model (GAM) for regression (自 R2021a 起)
`fitrgp`	Fit a Gaussian process regression (GPR) model
`fitrkernel`	Fit Gaussian kernel regression model using random feature expansion
`fitrlinear`	Fit linear regression model to high-dimensional data
`fitrm`	Fit repeated measures model
`fitrnet`	Train neural network regression model (自 R2021a 起)
`fitrsvm`	Fit a support vector machine regression model
`fitrtree`	Fit binary decision tree for regression
`fitsemigraph`	Label data using semi-supervised graph-based method (自 R2020b 起)
`fitsemiself`	Label data using semi-supervised self-training method (自 R2020b 起)
`fitSVMPosterior`	Fit posterior probabilities
`fitted`	Fitted responses from a linear mixed-effects model
`fitted`	Fitted responses from generalized linear mixed-effects model
`fixedEffects`	Estimates of fixed effects and related statistics
`fixedEffects`	Estimates of fixed effects and related statistics
`forecast`	Forecast response at time steps beyond available data (自 R2023b 起)
`fpdf`	F 概率密度函数
`fracfact`	Fractional factorial design
`fracfactgen`	Fractional factorial design generators
`friedman`	Friedman’s test
`frnd`	F 随机数
`fscchi2`	Univariate feature ranking for classification using chi-square tests (自 R2020a 起)
`fscmrmr`	Rank features for classification using minimum redundancy maximum relevance (MRMR) algorithm (自 R2019b 起)
`fscnca`	Feature selection using neighborhood component analysis for classification
`fsrftest`	Univariate feature ranking for regression using F-tests (自 R2020a 起)
`fsrmrmr`	Rank features for regression using minimum redundancy maximum relevance (MRMR) algorithm (自 R2022a 起)
`fsrnca`	Feature selection using neighborhood component analysis for regression
`fstat`	F mean and variance
`fsulaplacian`	Rank features for unsupervised learning using Laplacian scores (自 R2019b 起)
`fsurfht`	Interactive contour plot
`fullfact`	完全析因设计

G

`gagerr`	Gage repeatability and reproducibility study
`gamcdf`	Gamma cumulative distribution function
`gamfit`	Gamma parameter estimates
`gaminv`	Gamma inverse cumulative distribution function
`gamlike`	Gamma negative log-likelihood
`GammaDistribution`	gamma 概率分布对象
`gampdf`	Gamma probability density function
`gamrnd`	gamma 随机数
`gamstat`	Gamma mean and variance
`GapEvaluation`	Gap criterion clustering evaluation object
`gardnerAltmanPlot`	Gardner-Altman plot for two-sample effect size (自 R2022a 起)
`gather`	Gather properties of Statistics and Machine Learning Toolbox object from GPU (自 R2020b 起)
`gencfeatures`	Perform automated feature engineering for classification (自 R2021a 起)
`GeneralizedExtremeValueDistribution`	Generalized extreme value probability distribution object
`GeneralizedLinearMixedModel`	Generalized linear mixed-effects model class
`GeneralizedLinearModel`	Generalized linear regression model class
`GeneralizedParetoDistribution`	Generalized Pareto probability distribution object
`generateCode`	Generate C/C++ code using coder configurer
`generateFiles`	Generate MATLAB files for code generation using coder configurer
`generateLearnerDataTypeFcn`	Generate function that defines data types for fixed-point code generation (自 R2019b 起)
`genrfeatures`	Perform automated feature engineering for regression (自 R2021b 起)
`geocdf`	Geometric cumulative distribution function
`geoinv`	Geometric inverse cumulative distribution function
`geomean`	Geometric mean
`geopdf`	Geometric probability density function
`geornd`	Geometric random numbers
`geostat`	Geometric mean and variance
`get`	(Not Recommended) Access dataset array properties
`getlabels`	(Not Recommended) Access nominal or ordinal array labels
`getlevels`	(Not Recommended) Access nominal or ordinal array levels
`gevcdf`	Generalized extreme value cumulative distribution function
`gevfit`	Generalized extreme value parameter estimates
`gevinv`	Generalized extreme value inverse cumulative distribution function
`gevlike`	Generalized extreme value negative log-likelihood
`gevpdf`	Generalized extreme value probability density function
`gevrnd`	Generalized extreme value random numbers
`gevstat`	Generalized extreme value mean and variance
`gline`	以交互方式向绘图添加线条
`glmfit`	Fit generalized linear regression model
`glmval`	Generalized linear model values
`glyphplot`	Glyph plot
`gmdistribution`	Create Gaussian mixture model
`gname`	Add case names to plot
`gpcdf`	Generalized Pareto cumulative distribution function
`gpfit`	Generalized Pareto parameter estimates
`gpinv`	Generalized Pareto inverse cumulative distribution function
`gplike`	Generalized Pareto negative loglikelihood
`gplotmatrix`	Matrix of scatter plots by group
`gppdf`	Generalized Pareto probability density function
`gprnd`	Generalized Pareto random numbers
`gpstat`	Generalized Pareto mean and variance
`groupmeans`	Mean response estimates for analysis of variance (ANOVA) (自 R2022b 起)
`groupmeans`	Mean response estimates for multivariate analysis of variance (MANOVA) (自 R2023b 起)
`growTrees`	Train additional trees and add to ensemble
`grp2idx`	Create index vector from grouping variable
`grpstats`	Summary statistics organized by group
`grpstats`	Compute descriptive statistics of repeated measures data by group
`gscatter`	分组散点图

H

`HalfNormalDistribution`	Half-normal probability distribution object
`haltonset`	Halton quasirandom point set
`HamiltonianSampler`	Hamiltonian Monte Carlo (HMC) sampler
`harmmean`	Harmonic mean
`hazardratio`	Estimate Cox model hazard relative to baseline (自 R2021a 起)
`hist3`	Bivariate histogram plot
`histcounts`	Compute histogram bin counts for specified variables in baseline and target data for drift detection (自 R2022a 起)
`histfit`	具有分布拟合的直方图
`hmcSampler`	Hamiltonian Monte Carlo (HMC) sampler
`hmmdecode`	Hidden Markov model posterior state probabilities
`hmmestimate`	Hidden Markov model parameter estimates from emissions and states
`hmmgenerate`	Hidden Markov model states and emissions
`hmmtrain`	Hidden Markov model parameter estimates from emissions
`hmmviterbi`	Hidden Markov model most probable state path
`hnswSearcher`	Hierarchical Navigable Small Worlds (HNSW) approximate nearest neighbor search (自 R2024a 起)
`HoeffdingDriftDetectionMethod`	Incremental concept drift detector that utilizes Hoeffding's Bounds Drift Detection Method (HDDM) (自 R2022a 起)
`horzcat`	(Not Recommended) Horizontal concatenation for dataset arrays
`hougen`	豪根-瓦特森模型
`hygecdf`	Hypergeometric cumulative distribution function
`hygeinv`	Hypergeometric inverse cumulative distribution function
`hygepdf`	Hypergeometric probability density function
`hygernd`	Hypergeometric random numbers
`hygestat`	Hypergeometric mean and variance
`hyperparameters`	Variable descriptions for optimizing a fit function

I

`icdf`	逆累积分布函数
`iforest`	Fit isolation forest for anomaly detection (自 R2021b 起)
`inconsistent`	Inconsistency coefficient
`increaseB`	Increase reference data sets
`incrementalClassificationECOC`	Multiclass classification model using binary learners for incremental learning (自 R2022a 起)
`incrementalClassificationKernel`	Binary classification kernel model for incremental learning (自 R2022a 起)
`incrementalClassificationLinear`	Binary classification linear model for incremental learning (自 R2020b 起)
`incrementalClassificationNaiveBayes`	Naive Bayes classification model for incremental learning (自 R2021a 起)
`incrementalConceptDriftDetector`	Instantiate incremental concept drift detector (自 R2022a 起)
`incrementalDriftAwareLearner`	Construct drift-aware model for incremental learning (自 R2022b 起)
`incrementalLearner`	Convert robust random cut forest model to incremental learner (自 R2023b 起)
`incrementalLearner`	Convert one-class SVM model to incremental learner (自 R2023b 起)
`incrementalLearner`	Convert support vector machine (SVM) regression model to incremental learner (自 R2020b 起)
`incrementalLearner`	Convert linear regression model to incremental learner (自 R2020b 起)
`incrementalLearner`	Convert kernel regression model to incremental learner (自 R2022a 起)
`incrementalLearner`	Convert naive Bayes classification model to incremental learner (自 R2021a 起)
`incrementalLearner`	Convert binary classification support vector machine (SVM) model to incremental learner (自 R2020b 起)
`incrementalLearner`	Convert linear model for binary classification to incremental learner (自 R2020b 起)
`incrementalLearner`	Convert multiclass error-correcting output codes (ECOC) model to incremental learner (自 R2022a 起)
`incrementalLearner`	Convert kernel model for binary classification to incremental learner (自 R2022a 起)
`incrementalOneClassSVM`	One-class support vector machine (SVM) model for incremental anomaly detection (自 R2023b 起)
`incrementalRegressionKernel`	Kernel regression model for incremental learning (自 R2022a 起)
`incrementalRegressionLinear`	Linear regression model for incremental learning (自 R2020b 起)
`incrementalRobustRandomCutForest`	Robust random cut forest model for incremental anomaly detection (自 R2023b 起)
`interactionplot`	Interaction plot for grouped data
`intersect`	(Not Recommended) Set intersection for dataset array observations
`InverseGaussianDistribution`	逆高斯概率分布对象
`invpred`	Inverse prediction
`iqr`	Interquartile range of probability distribution
`isanomaly`	Find anomalies in data using isolation forest (自 R2021b 起)
`isanomaly`	Find anomalies in data using robust random cut forest (自 R2023a 起)
`isanomaly`	Find anomalies in data using local outlier factor (自 R2022b 起)
`isanomaly`	Find anomalies in data using one-class support vector machine (SVM) (自 R2022b 起)
`isanomaly`	Find anomalies in data using robust random cut forest (RRCF) for incremental learning (自 R2023b 起)
`isanomaly`	Find anomalies in data using one-class support vector machine (SVM) for incremental learning (自 R2023b 起)
`isempty`	(Not Recommended) True for empty dataset array
`islevel`	(Not Recommended) Determine if levels are in nominal or ordinal array
`ismember`	(Not Recommended) Dataset array elements that are members of set
`ismissing`	(Not Recommended) Find dataset array elements with missing values
`IsolationForest`	Isolation forest for anomaly detection (自 R2021b 起)
`iwishrnd`	Inverse Wishart random numbers

J

`jackknife`	Jackknife sampling
`jbtest`	Jarque-Bera test
`johnsrnd`	Johnson system random numbers
`join`	(Not Recommended) Merge dataset array observations

K

`KDTreeSearcher`	Create Kd-tree nearest neighbor searcher
`KernelDistribution`	核概率分布对象
`kfoldEdge`	Classification edge for cross-validated classification model
`kfoldEdge`	Classification edge for cross-validated ECOC model
`kfoldEdge`	Classification edge for observations not used for training
`kfoldEdge`	Classification edge for observations not used for training
`kfoldEdge`	Classification edge for cross-validated kernel classification model
`kfoldEdge`	Classification edge for cross-validated kernel ECOC model
`kfoldfun`	Cross-validate function for regression
`kfoldfun`	Cross-validate function for classification
`kfoldfun`	Cross-validate function using cross-validated ECOC model
`kfoldLoss`	Regression loss for observations not used in training
`kfoldLoss`	Regression loss for cross-validated kernel regression model
`kfoldLoss`	Loss for cross-validated partitioned regression model
`kfoldLoss`	Classification loss for cross-validated classification model
`kfoldLoss`	Classification loss for cross-validated ECOC model
`kfoldLoss`	Classification loss for observations not used in training
`kfoldLoss`	Classification loss for observations not used in training
`kfoldLoss`	Classification loss for cross-validated kernel classification model
`kfoldLoss`	Classification loss for cross-validated kernel ECOC model
`kfoldMargin`	Classification margins for cross-validated classification model
`kfoldMargin`	Classification margins for cross-validated ECOC model
`kfoldMargin`	Classification margins for observations not used in training
`kfoldMargin`	Classification margins for observations not used in training
`kfoldMargin`	Classification margins for cross-validated kernel classification model
`kfoldMargin`	Classification margins for cross-validated kernel ECOC model
`kfoldPredict`	Predict responses for observations not used for training
`kfoldPredict`	Predict responses for observations in cross-validated kernel regression model
`kfoldPredict`	Predict responses for observations in cross-validated regression model
`kfoldPredict`	Classify observations in cross-validated classification model
`kfoldPredict`	Classify observations in cross-validated ECOC model
`kfoldPredict`	Predict labels for observations not used for training
`kfoldPredict`	Predict labels for observations not used for training
`kfoldPredict`	Classify observations in cross-validated kernel classification model
`kfoldPredict`	Classify observations in cross-validated kernel ECOC model
`kmeans`	k 均值聚类
`kmedoids`	k-medoids clustering
`knnsearch`	Find k-nearest neighbors using searcher object
`knnsearch`	Find k-nearest neighbors using input data
`kruskalwallis`	Kruskal-Wallis test
`ksdensity`	Kernel smoothing function estimate for univariate and bivariate data
`kstest`	One-sample Kolmogorov-Smirnov test
`kstest2`	Two-sample Kolmogorov-Smirnov test
`kurtosis`	Kurtosis

L

`lasso`	Lasso or elastic net regularization for linear models
`lassoglm`	Lasso or elastic net regularization for generalized linear models
`lassoPlot`	Trace plot of lasso fit
`learnerCoderConfigurer`	Create coder configurer of machine learning model
`length`	(Not Recommended) Length of dataset array
`levelcounts`	(Not Recommended) Element counts by level of a nominal or ordinal array
`leverage`	Leverage
`lhsdesign`	拉丁超立方样本
`lhsnorm`	来自正态分布的拉丁超立方样本
`lillietest`	Lilliefors test
`lime`	Local interpretable model-agnostic explanations (LIME) (自 R2020b 起)
`LinearMixedModel`	Linear mixed-effects model
`LinearModel`	Linear regression model
`linhyptest`	Linear hypothesis test
`linhyptest`	Linear hypothesis tests on Cox model coefficients (自 R2021a 起)
`linkage`	Agglomerative hierarchical cluster tree
`loadCompactModel`	(Removed) Reconstruct model object from saved model for code generation
`loadLearnerForCoder`	Reconstruct model object from saved model for code generation (自 R2019b 起)
`LocalOutlierFactor`	Local outlier factor model for anomaly detection (自 R2022b 起)
`lof`	Create local outlier factor model for anomaly detection (自 R2022b 起)
`LogisticDistribution`	逻辑概率分布对象
`LoglogisticDistribution`	Loglogistic probability distribution object
`logncdf`	对数正态累积分布函数
`lognfit`	Lognormal parameter estimates
`logninv`	对数正态逆累积分布函数
`lognlike`	Lognormal negative loglikelihood
`LognormalDistribution`	对数正态概率分布对象
`lognpdf`	对数正态概率密度函数
`lognrnd`	对数正态随机数
`lognstat`	Lognormal mean and variance
`logp`	Log unconditional probability density for discriminant analysis classifier
`logp`	Log unconditional probability density for naive Bayes classifier
`logp`	Log unconditional probability density of naive Bayes classification model for incremental learning (自 R2021a 起)
`LoguniformDistribution`	Loguniform probability distribution object (自 R2021b 起)
`loss`	Regression loss for linear regression models
`loss`	Classification loss for Gaussian kernel classification model
`loss`	Regression error for support vector machine regression model
`loss`	Regression loss for Gaussian kernel regression model
`loss`	Regression error for Gaussian process regression model
`loss`	Regression error for regression tree model
`loss`	Regression error for regression ensemble model
`loss`	Regression loss for generalized additive model (GAM) (自 R2021a 起)
`loss`	Loss for regression neural network (自 R2021a 起)
`loss`	Regression or classification error of incremental drift-aware learner (自 R2022b 起)
`loss`	Loss of kernel incremental learning model on batch of data (自 R2022a 起)
`loss`	Loss of linear incremental learning model on batch of data (自 R2020b 起)
`loss`	Loss at each horizon step (自 R2023b 起)
`loss`	Evaluate accuracy of learned feature weights on test data
`loss`	Classification loss for classification tree model
`loss`	Classification loss for discriminant analysis classifier
`loss`	Classification loss for naive Bayes classifier
`loss`	Loss of k-nearest neighbor classifier
`loss`	Find classification error for support vector machine (SVM) classifier
`loss`	Classification loss for multiclass error-correcting output codes (ECOC) model
`loss`	Classification loss for linear classification models
`loss`	Classification loss for classification ensemble model
`loss`	Classification loss for generalized additive model (GAM) (自 R2021a 起)
`loss`	Classification loss for neural network classifier (自 R2021a 起)
`loss`	Loss of ECOC incremental learning classification model on batch of data (自 R2022a 起)
`loss`	Loss of naive Bayes incremental learning classification model on batch of data (自 R2021a 起)
`loss`	Classification loss adjusted by fairness threshold (自 R2023a 起)
`loss`	Evaluate accuracy of learned feature weights on test data
`lowerparams`	Lower Pareto tail parameters
`lsline`	向散点图添加最小二乘线条

M

`mad`	Mean or median absolute deviation
`mahal`	Mahalanobis distance to Gaussian mixture component
`mahal`	Mahalanobis distance to reference samples
`mahal`	Mahalanobis distance to class means of discriminant analysis classifier
`maineffectsplot`	Main effects plot for grouped data
`makecdiscr`	Construct discriminant analysis classifier from parameters
`makedist`	创建概率分布对象
`manova`	Multivariate analysis of variance
`manova`	Multivariate analysis of variance (MANOVA) results (自 R2023b 起)
`manova1`	One-way multivariate analysis of variance
`manovacluster`	Dendrogram of group mean clusters following MANOVA
`margin`	Classification margins for Gaussian kernel classification model
`margin`	Classification margins for classification tree model
`margin`	Classification margins for discriminant analysis classifier
`margin`	Classification margins for naive Bayes classifier
`margin`	Margin of k-nearest neighbor classifier
`margin`	Find classification margins for support vector machine (SVM) classifier
`margin`	Classification margins for multiclass error-correcting output codes (ECOC) model
`margin`	Classification margins for linear classification models
`margin`	Classification margins for classification ensemble model
`margin`	分类间隔
`margin`	分类间隔
`margin`	Classification margins for generalized additive model (GAM) (自 R2021a 起)
`margin`	Classification margins for neural network classifier (自 R2021a 起)
`margmean`	Estimate marginal means
`mat2dataset`	(Not Recommended) Convert matrix to dataset array
`mauchly`	Mauchly’s test for sphericity
`mdscale`	Nonclassical multidimensional scaling
`mdsprox`	Multidimensional scaling of proximity matrix
`mdsprox`	Multidimensional scaling of proximity matrix
`mean`	概率分布的均值
`meanEffectSize`	One-sample or two-sample effect size computations (自 R2022a 起)
`meanMargin`	Mean classification margin
`meanMargin`	Mean classification margin
`median`	Median of probability distribution
`mergelevels`	(Not Recommended) Merge levels of nominal or ordinal arrays
`mhsample`	Metropolis-Hastings sample
`mle`	Maximum likelihood estimates
`mlecov`	Asymptotic covariance of maximum likelihood estimators
`mnpdf`	Multinomial probability density function
`mnrfit`	(Not recommended) Multinomial logistic regression
`mnrnd`	Multinomial random numbers
`mnrval`	(Not recommended) Multinomial logistic regression values
`moment`	Central moment
`multcompare`	Multiple comparison test
`multcompare`	Multiple comparison of means for analysis of variance (ANOVA) (自 R2022b 起)
`multcompare`	Multiple comparison of estimated marginal means
`multcompare`	Multiple comparison of marginal means for multiple analysis of variance (MANOVA) (自 R2023b 起)
`MultinomialDistribution`	Multinomial probability distribution object
`MultinomialRegression`	Multinomial regression model (自 R2023a 起)
`multivarichart`	Multivari chart for grouped data
`mvksdensity`	Kernel smoothing function estimate for multivariate data
`mvncdf`	Multivariate normal cumulative distribution function
`mvnpdf`	多元正态概率密度函数
`mvnrnd`	多元正态随机数
`mvregress`	Multivariate linear regression
`mvregresslike`	Negative log-likelihood for multivariate regression
`mvtcdf`	Multivariate t cumulative distribution function
`mvtpdf`	Multivariate t probability density function
`mvtrnd`	Multivariate t random numbers

N

`NakagamiDistribution`	Nakagami 概率分布对象
`nancov`	(Not recommended) Covariance ignoring `NaN` values
`nanmax`	(Not recommended) Maximum, ignoring `NaN` values
`nanmean`	（不推荐）均值，忽略 `NaN` 值
`nanmedian`	(Not recommended) Median, ignoring `NaN` values
`nanmin`	(Not recommended) Minimum, ignoring `NaN` values
`nanstd`	(Not recommended) Standard deviation, ignoring `NaN` values
`nansum`	（不推荐）总和，忽略 `NaN` 值
`nanvar`	(Not recommended) Variance, ignoring `NaN` values
`nbincdf`	Negative binomial cumulative distribution function
`nbinfit`	Negative binomial parameter estimates
`nbininv`	Negative binomial inverse cumulative distribution function
`nbinpdf`	Negative binomial probability density function
`nbinrnd`	Negative binomial random numbers
`nbinstat`	Negative binomial mean and variance
`ncfcdf`	Noncentral F cumulative distribution function
`ncfinv`	Noncentral F inverse cumulative distribution function
`ncfpdf`	Noncentral F probability density function
`ncfrnd`	Noncentral F random numbers
`ncfstat`	Noncentral F mean and variance
`nctcdf`	Noncentral t cumulative distribution function
`nctinv`	Noncentral t inverse cumulative distribution function
`nctpdf`	Noncentral t probability density function
`nctrnd`	Noncentral t random numbers
`nctstat`	Noncentral t mean and variance
`ncx2cdf`	Noncentral chi-square cumulative distribution function
`ncx2inv`	Noncentral chi-square inverse cumulative distribution function
`ncx2pdf`	Noncentral chi-square probability density function
`ncx2rnd`	Noncentral chi-square random numbers
`ncx2stat`	Noncentral chi-square mean and variance
`ndims`	(Not Recommended) Number of dimensions of dataset array
`nearcorr`	Compute nearest correlation matrix by minimizing Frobenius distance (自 R2019b 起)
`NegativeBinomialDistribution`	负二项分布对象
`negloglik`	Negative loglikelihood of probability distribution
`net`	Generate quasirandom point set
`nLinearCoeffs`	Number of nonzero linear coefficients in discriminant analysis classifier
`nlinfit`	非线性回归
`nlintool`	Interactive nonlinear regression
`nlmefit`	Nonlinear mixed-effects estimation
`nlmefitsa`	Fit nonlinear mixed-effects model with stochastic EM algorithm
`nlparci`	Nonlinear regression parameter confidence intervals
`nlpredci`	Nonlinear regression prediction confidence intervals
`nnmf`	Nonnegative matrix factorization
`nodeVariableRange`	Retrieve variable range of decision tree node (自 R2020a 起)
`nominal`	(Not Recommended) Arrays for nominal data
`NonLinearModel`	Nonlinear regression model
`NormalDistribution`	正态概率分布对象
`normcdf`	正态累积分布函数
`normfit`	Normal parameter estimates
`norminv`	正态逆累积分布函数
`normlike`	Normal negative loglikelihood
`normpdf`	正态概率密度函数
`normplot`	Normal probability plot
`normrnd`	正态随机数
`normspec`	Normal density plot shading between specifications
`normstat`	Normal mean and variance
`nsegments`	Number of segments in piecewise distribution
`numel`	(Not Recommended) Number of elements in dataset array

O

`ocsvm`	Fit one-class support vector machine (SVM) model for anomaly detection (自 R2022b 起)
`OneClassSVM`	One-class support vector machine (SVM) for anomaly detection (自 R2022b 起)
`onehotdecode`	Decode probability vectors into class labels (自 R2021b 起)
`onehotencode`	Encode data labels into one-hot vectors (自 R2021b 起)
`oobEdge`	Out-of-bag classification edge for bagged classification ensemble model
`oobError`	Out-of-bag error
`oobLoss`	Out-of-bag error for bagged regression ensemble model
`oobLoss`	Out-of-bag classification loss for bagged classification ensemble model
`oobMargin`	Out-of-bag classification margins of bagged classification ensemble
`oobMargin`	Out-of-bag margins
`oobMeanMargin`	Out-of-bag mean margins
`oobPermutedPredictorImportance`	Out-of-bag predictor importance estimates for random forest of regression trees by permutation
`oobPermutedPredictorImportance`	Out-of-bag predictor importance estimates for random forest of classification trees by permutation
`oobPredict`	Ensemble predictions for out-of-bag observations
`oobPredict`	Predict out-of-bag responses of bagged regression ensemble
`oobPredict`	Predict out-of-bag labels and scores of bagged classification ensemble
`oobQuantileError`	Out-of-bag quantile loss of bag of regression trees
`oobQuantilePredict`	Quantile predictions for out-of-bag observations from bag of regression trees
`optimalleaforder`	Optimal leaf ordering for hierarchical clustering
`optimizableVariable`	Variable description for `bayesopt` or other optimizers
`ordinal`	(Not Recommended) Arrays for ordinal data
`outlierMeasure`	Outlier measure for data in ensemble of decision trees

P

`parallelcoords`	Parallel coordinates plot
`paramci`	Confidence intervals for probability distribution parameters
`paretotails`	Piecewise distribution with Pareto tails
`partialcorr`	Linear or rank partial correlation coefficients
`partialcorri`	Partial correlation coefficients adjusted for internal variables
`partialDependence`	Compute partial dependence (自 R2020b 起)
`PartitionedDirectForecaster`	Cross-validated direct forecasting model (自 R2023b 起)
`pca`	原始数据的主成分分析
`pcacov`	对协方差矩阵的主成分分析
`pcares`	Residuals from principal component analysis
`pdf`	概率密度函数
`pdf`	高斯混合分布的概率密度函数
`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
`pearscdf`	Pearson cumulative distribution function (自 R2023b 起)
`pearspdf`	Pearson probability density function (自 R2023b 起)
`pearsrnd`	Pearson system random numbers
`perfcurve`	Receiver operating characteristic (ROC) curve or other performance curve for classifier output
`permutationImportance`	Predictor importance by permutation (自 R2024a 起)
`perObservationLoss`	Per observation regression or classification error of incremental drift-aware learner (自 R2022b 起)
`perObservationLoss`	Per observation regression error of model for incremental learning (自 R2022a 起)
`perObservationLoss`	Per observation classification error of model for incremental learning (自 R2022a 起)
`PiecewiseLinearDistribution`	Piecewise linear probability distribution object
`plot`	Plot probability distribution object (自 R2022b 起)
`plot`	Plot clustering evaluation object criterion values
`plot`	Plot data with optional grouping
`plot`	Scatter plot or added variable plot of linear regression model
`plot`	Plot results of local interpretable model-agnostic explanations (LIME) (自 R2020b 起)
`plot`	Plot Shapley values using bar graphs (自 R2021a 起)
`plot`	Plot Bayesian optimization results
`plot`	Plot bar graph of fairness metric (自 R2022b 起)
`plot`	Plot receiver operating characteristic (ROC) curves and other performance curves (自 R2022a 起)
`plotAdded`	Added variable plot of linear regression model
`plotAdjustedResponse`	Adjusted response plot of linear regression model
`plotComparisons`	Interactive plot of multiple comparisons of means for analysis of variance (ANOVA) (自 R2022b 起)
`plotDiagnostics`	Plot observation diagnostics of linear regression model
`plotDiagnostics`	Plot observation diagnostics of generalized linear regression model
`plotDiagnostics`	Plot diagnostics of nonlinear regression model
`plotDriftStatus`	Plot p-values and confidence intervals for variables tested for data drift (自 R2022a 起)
`plotEffects`	Plot main effects of predictors in linear regression model
`plotEmpiricalCDF`	Plot empirical cumulative distribution function (ecdf) of a variable specified for data drift detection (自 R2022a 起)
`plotHistogram`	Plot histogram of a variable specified for data drift detection (自 R2022a 起)
`plotInteraction`	Plot interaction effects of two predictors in linear regression model
`plotLocalEffects`	Plot local effects of terms in generalized additive model (GAM) (自 R2021a 起)
`plotPartialDependence`	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
`plotPermutationResults`	Plot histogram of permutation results for a variable specified for data drift detection (自 R2022a 起)
`plotprofile`	Plot expected marginal means with optional grouping
`plotprofile`	Plot MANOVA response variable means with grouping (自 R2023b 起)
`plotResiduals`	Plot residuals of linear regression model
`plotResiduals`	Plot residuals of linear mixed-effects model
`plotResiduals`	Plot residuals of generalized linear regression model
`plotResiduals`	Plot residuals of multinomial regression model (自 R2023a 起)
`plotResiduals`	Plot residuals of generalized linear mixed-effects model
`plotResiduals`	Plot residuals of nonlinear regression model
`plotSlice`	Plot of slices through fitted linear regression surface
`plotSlice`	Plot of slices through fitted generalized linear regression surface
`plotSlice`	Plot of slices through fitted multinomial regression surface (自 R2023a 起)
`plotSlice`	Plot of slices through fitted nonlinear regression surface
`plotSurvival`	Plot survival function of Cox proportional hazards model (自 R2021a 起)
`plsregress`	Partial least-squares (PLS) regression
`poisscdf`	泊松累积分布函数
`poissfit`	泊松参数估计
`poissinv`	Poisson inverse cumulative distribution function
`PoissonDistribution`	泊松概率分布对象
`poisspdf`	泊松概率密度函数
`poissrnd`	来自泊松分布的随机数
`poisstat`	Poisson mean and variance
`polyconf`	Polynomial confidence intervals
`polyfit`	多项式曲线拟合
`polytool`	Interactive polynomial fitting
`posterior`	Posterior probability of Gaussian mixture component
`postFitStatistics`	Compute post-fit statistics for the exact Gaussian process regression model
`ppca`	Probabilistic principal component analysis
`predict`	Compute predicted values given predictor values
`predict`	Predict responses of linear regression model
`predict`	Predict response of linear regression model
`predict`	Predict response of linear mixed-effects model
`predict`	Predict responses of generalized linear regression model
`predict`	Predict responses of multinomial regression model (自 R2023a 起)
`predict`	Predict labels for linear classification models
`predict`	Classify observations using multiclass error-correcting output codes (ECOC) model
`predict`	Predict labels for Gaussian kernel classification model
`predict`	Predict response of generalized linear mixed-effects model
`predict`	Predict response of nonlinear regression model
`predict`	Predict responses using support vector machine regression model
`predict`	Predict responses for Gaussian kernel regression model
`predict`	Predict response of Gaussian process regression model
`predict`	Predict responses using regression tree model
`predict`	Predict responses using regression ensemble model
`predict`	Predict responses using ensemble of bagged decision trees
`predict`	Predict responses using ensemble of bagged decision trees
`predict`	Predict responses using generalized additive model (GAM) (自 R2021a 起)
`predict`	Predict responses using regression neural network (自 R2021a 起)
`predict`	Predict responses for new observations from incremental drift-aware learning model (自 R2022b 起)
`predict`	Predict responses for new observations from kernel incremental learning model (自 R2022a 起)
`predict`	Predict responses for new observations from linear incremental learning model (自 R2020b 起)
`predict`	Predict response at time steps in observed test data (自 R2023b 起)
`predict`	Predict responses using neighborhood component analysis (NCA) regression model
`predict`	Predict labels using classification tree model
`predict`	Predict labels using discriminant analysis classifier
`predict`	Classify observations using naive Bayes classifier
`predict`	Predict labels using k-nearest neighbor classification model
`predict`	Classify observations using support vector machine (SVM) classifier
`predict`	Predict labels using classification ensemble model
`predict`	Classify observations using generalized additive model (GAM) (自 R2021a 起)
`predict`	Classify observations using neural network classifier (自 R2021a 起)
`predict`	Predict responses for new observations from ECOC incremental learning classification model (自 R2022a 起)
`predict`	Predict responses for new observations from naive Bayes incremental learning classification model (自 R2021a 起)
`predict`	Label new data using semi-supervised graph-based classifier (自 R2020b 起)
`predict`	Label new data using semi-supervised self-trained classifier (自 R2020b 起)
`predict`	Predicted labels adjusted by fairness threshold (自 R2023a 起)
`predict`	Predict responses using neighborhood component analysis (NCA) classifier
`predictConstraints`	Predict coupled constraint violations at a set of points
`predictError`	Predict error value at a set of points
`predictObjective`	Predict objective function at a set of points
`predictObjectiveEvaluationTime`	Predict objective function run times at a set of points
`predictorImportance`	Estimates of predictor importance for regression tree
`predictorImportance`	Estimates of predictor importance for regression ensemble of decision trees
`predictorImportance`	Estimates of predictor importance for classification tree
`predictorImportance`	Estimates of predictor importance for classification ensemble of decision trees
`preparedPredictors`	Obtain prepared data used for training or testing in direct forecasting (自 R2023b 起)
`probplot`	Probability plots
`procrustes`	Procrustes analysis
`proflik`	Profile likelihood function for probability distribution
`proximity`	Proximity matrix for data in ensemble of decision trees
`prune`	Produce sequence of regression subtrees by pruning regression tree
`prune`	Produce sequence of classification subtrees by pruning classification tree

Q

`qqplot`	Quantile-quantile plot
`qrand`	Generate quasirandom points from stream
`qrandstream`	Quasirandom number stream
`quantileError`	Quantile loss using bag of regression trees
`quantilePredict`	Predict response quantile using bag of regression trees

R

`rand`	均匀分布的随机数
`rand`	Generate quasirandom points from stream
`randg`	Gamma random numbers with unit scale
`random`	随机数
`random`	高斯混合分布的随机变量
`random`	Generate new random response values given predictor values
`random`	Simulate responses with random noise for linear regression model
`random`	Generate random responses from fitted linear mixed-effects model
`random`	Simulate responses with random noise for generalized linear regression model
`random`	Generate random responses from fitted multinomial regression model (自 R2023a 起)
`random`	Generate random responses from fitted generalized linear mixed-effects model
`random`	Simulate responses for nonlinear regression model
`randomEffects`	Estimates of random effects and related statistics
`randomEffects`	Estimates of random effects and related statistics
`randsample`	随机样本
`randtool`	Interactive random number generation
`range`	值的范围
`rangesearch`	Find all neighbors within specified distance using searcher object
`rangesearch`	Find all neighbors within specified distance using input data
`ranksum`	威尔科克森秩和检验
`ranova`	Repeated measures analysis of variance
`raylcdf`	瑞利累积分布函数
`RayleighDistribution`	瑞利概率分布对象
`raylfit`	瑞利参数估计值
`raylinv`	Rayleigh inverse cumulative distribution function
`raylpdf`	瑞利概率密度函数
`raylrnd`	瑞利随机数
`raylstat`	Rayleigh mean and variance
`rcoplot`	残差观测顺序图
`ReconstructionICA`	Feature extraction by reconstruction ICA
`reduceDimensions`	Reduce dimensions of Sobol point set
`refcurve`	Add reference curve to plot
`refit`	Refit generalized linear mixed-effects model
`refit`	Refit neighborhood component analysis (NCA) model for regression
`refit`	Refit neighborhood component analysis (NCA) model for classification
`refline`	将参考线添加到绘图中
`regress`	多重线性回归
`RegressionBaggedEnsemble`	Regression ensemble grown by resampling
`RegressionEnsemble`	Ensemble regression
`RegressionGAM`	Generalized additive model (GAM) for regression (自 R2021a 起)
`RegressionGP`	Gaussian process regression model
`RegressionKernel`	Gaussian kernel regression model using random feature expansion
`RegressionLinear`	Linear regression model for high-dimensional data
`RegressionLinearCoderConfigurer`	Coder configurer for linear regression model with high-dimensional data (自 R2019b 起)
`RegressionNeuralNetwork`	Neural network model for regression (自 R2021a 起)
`RegressionPartitionedEnsemble`	Cross-validated regression ensemble
`RegressionPartitionedGAM`	Cross-validated generalized additive model (GAM) for regression (自 R2021a 起)
`RegressionPartitionedGP`	Cross-validated Gaussian process regression (GPR) model (自 R2022b 起)
`RegressionPartitionedKernel`	Cross-validated kernel model for regression
`RegressionPartitionedLinear`	Cross-validated linear regression model for high-dimensional data
`RegressionPartitionedModel`	Cross-validated regression model
`RegressionPartitionedNeuralNetwork`	Cross-validated regression neural network model (自 R2023b 起)
`RegressionPartitionedSVM`	Cross-validated support vector machine regression model
`RegressionSVM`	Support vector machine regression model
`RegressionSVMCoderConfigurer`	Coder configurer for support vector machine (SVM) regression model
`RegressionTree`	Regression tree
`RegressionTreeCoderConfigurer`	Coder configurer of binary decision tree model for regression (自 R2019b 起)
`regstats`	Regression diagnostics
`regularize`	Find optimal weights for learners in regression ensemble
`relieff`	Rank importance of predictors using ReliefF or RReliefF algorithm
`removeLearners`	Remove members of compact regression ensemble
`removeLearners`	Remove members of compact classification ensemble
`removeTerms`	Remove terms from linear regression model
`removeTerms`	Remove terms from generalized linear regression model
`reorderlevels`	(Not Recommended) Reorder levels of nominal or ordinal arrays
`repartition`	Repartition data for cross-validation
`RepeatedMeasuresModel`	Repeated measures model object
`replacedata`	(Not Recommended) Replace dataset variables
`replaceWithMissing`	(Not Recommended) Insert missing data indicators into a dataset array
`report`	Generate fairness metrics report (自 R2022b 起)
`reset`	Reset state
`reset`	Reset incremental robust random cut forest model (自 R2023b 起)
`reset`	Reset incremental one-class SVM model (自 R2023b 起)
`reset`	Reset incremental drift-aware learner (自 R2022b 起)
`reset`	Reset incremental regression model (自 R2022a 起)
`reset`	Reset incremental concept drift detector (自 R2022a 起)
`reset`	Reset incremental classification model (自 R2022a 起)
`residuals`	Residuals of fitted linear mixed-effects model
`residuals`	Residuals of fitted generalized linear mixed-effects model
`response`	Response vector of the linear mixed-effects model
`response`	Response vector of generalized linear mixed-effects model
`resubEdge`	Resubstitution classification edge for classification tree model
`resubEdge`	Resubstitution classification edge for discriminant analysis classifier
`resubEdge`	Resubstitution classification edge
`resubEdge`	Resubstitution classification edge for multiclass error-correcting output codes (ECOC) model
`resubEdge`	Resubstitution classification edge for classification ensemble model
`resubLoss`	Resubstitution loss for support vector machine regression model
`resubLoss`	Resubstitution regression loss
`resubLoss`	Resubstitution loss for regression tree model
`resubLoss`	Resubstitution loss for regression ensemble model
`resubLoss`	Resubstitution classification loss for classification tree model
`resubLoss`	Resubstitution classification loss for discriminant analysis classifier
`resubLoss`	Resubstitution classification loss
`resubLoss`	Resubstitution classification loss for multiclass error-correcting output codes (ECOC) model
`resubLoss`	Resubstitution classification loss for classification ensemble model
`resubMargin`	Resubstitution classification margins for classification tree model
`resubMargin`	Resubstitution classification margins for discriminant analysis classifier
`resubMargin`	Resubstitution classification margin
`resubMargin`	Resubstitution classification margins for multiclass error-correcting output codes (ECOC) model
`resubMargin`	Resubstitution classification margins for classification ensemble model
`resubPredict`	Predict resubstitution response of support vector machine regression model
`resubPredict`	Predict responses for training data using trained regression model
`resubPredict`	Predict response of regression tree by resubstitution
`resubPredict`	Predict response of regression ensemble by resubstitution
`resubPredict`	Classify observations in classification tree by resubstitution
`resubPredict`	Classify observations in discriminant analysis classifier by resubstitution
`resubPredict`	Classify training data using trained classifier
`resubPredict`	Classify observations in multiclass error-correcting output codes (ECOC) model
`resubPredict`	Classify observations in classification ensemble by resubstitution
`resume`	Resume training of Gaussian kernel classification model
`resume`	Resume training support vector machine regression model
`resume`	Resume training of Gaussian kernel regression model
`resume`	Resume training of regression ensemble model
`resume`	Resume training of cross-validated regression ensemble model
`resume`	Resume training of generalized additive model (GAM) (自 R2021a 起)
`resume`	Resume a Bayesian optimization
`resume`	Resume training support vector machine (SVM) classifier
`resume`	Resume training of classification ensemble model
`resume`	Resume training of cross-validated classification ensemble model
`rica`	Feature extraction by using reconstruction ICA
`RicianDistribution`	莱斯概率分布对象
`ridge`	Ridge regression
`robustcov`	Robust multivariate covariance and mean estimate
`robustdemo`	Interactive robust regression
`robustfit`	Fit robust linear regression
`RobustRandomCutForest`	Robust random cut forest model for anomaly detection (自 R2023a 起)
`rocmetrics`	Receiver operating characteristic (ROC) curve and performance metrics for binary and multiclass classifiers (自 R2022a 起)
`rotatefactors`	Rotate factor loadings
`rowexch`	Row exchange
`rrcforest`	Fit robust random cut forest model for anomaly detection (自 R2023a 起)
`rsmdemo`	Interactive response surface demonstration
`rstool`	Interactive response surface modeling
`runstest`	Run test for randomness

S

`sampsizepwr`	Sample size and power of test
`saveCompactModel`	(Removed) Save model object in file for code generation
`saveLearnerForCoder`	Save model object in file for code generation (自 R2019b 起)
`scatterhist`	Scatter plot with marginal histograms
`scramble`	Scramble quasirandom point set
`segment`	Piecewise distribution segments containing input values
`selectFeatures`	Select important features for NCA classification or regression (自 R2023b 起)
`selectModels`	Select fitted regularized linear regression models
`selectModels`	Choose subset of multiclass ECOC models composed of binary `ClassificationLinear` learners
`selectModels`	Choose subset of regularized, binary linear classification models
`SemiSupervisedGraphModel`	Semi-supervised graph-based model for classification (自 R2020b 起)
`SemiSupervisedSelfTrainingModel`	Semi-supervised self-trained model for classification (自 R2020b 起)
`sequentialfs`	Sequential feature selection using custom criterion
`set`	(Not Recommended) Set and display dataset array properties
`setDefaultYfit`	Set default value for `predict`
`setdiff`	(Not Recommended) Set difference for dataset array observations
`setlabels`	(Not Recommended) Assign labels to levels of nominal or ordinal arrays
`setxor`	(Not Recommended) Set exclusive or for dataset array observations
`shapley`	Shapley values (自 R2021a 起)
`shrink`	Prune regression ensemble
`signrank`	Wilcoxon signed rank test
`signtest`	Sign test
`silhouette`	Silhouette plot
`SilhouetteEvaluation`	Silhouette criterion clustering evaluation object
`single`	(Not Recommended) Convert dataset variables to single array
`size`	(Not Recommended) Size of dataset array
`skewness`	Skewness
`slicesample`	Slice sampler
`sobolset`	Sobol quasirandom point set
`sortClasses`	Sort classes of confusion matrix chart
`sortrows`	(Not Recommended) Sort rows of dataset array
`sparsefilt`	Feature extraction by using sparse filtering
`SparseFiltering`	Feature extraction by sparse filtering
`spectralcluster`	Spectral clustering (自 R2019b 起)
`squareform`	Format distance matrix
`StableDistribution`	稳定概率分布对象
`stack`	(Not Recommended) Stack dataset array from multiple variables into single variable
`statget`	Access values in statistics options structure
`stats`	Analysis of variance (ANOVA) table (自 R2022b 起)
`stats`	Multivariate analysis of variance (MANOVA) table (自 R2023b 起)
`statset`	Create statistics options structure
`std`	概率分布的标准差
`step`	Improve linear regression model by adding or removing terms
`step`	Improve generalized linear regression model by adding or removing terms
`stepwise`	Interactive stepwise regression
`stepwisefit`	Fit linear regression model using stepwise regression
`stepwiseglm`	Create generalized linear regression model by stepwise regression
`stepwiselm`	Perform stepwise regression
`struct2dataset`	(Not Recommended) Convert structure array to dataset array
`subsasgn`	(Not Recommended) Subscripted assignment to dataset array
`subsref`	(Not Recommended) Subscripted reference for dataset array
`summary`	(Not Recommended) Print summary of dataset array
`summary`	Summary table for `DriftDiagnostics` object (自 R2022a 起)
`surfht`	Interactive contour plot
`surrogateAssociation`	Mean predictive measure of association for surrogate splits in regression tree
`surrogateAssociation`	Mean predictive measure of association for surrogate splits in classification tree
`survival`	Calculate survival of Cox proportional hazards model (自 R2021a 起)
`swarmchart`	Visualize Shapley values using swarm scatter charts (自 R2024a 起)

T

`table2dataset`	(Not Recommended) Convert table to dataset array
`tabulate`	频数表
`tblread`	Read tabular data from file
`tblwrite`	Write tabular data to file
`tcdf`	Student t 累积分布函数
`tdfread`	Read tab-delimited file
`templateDiscriminant`	Discriminant analysis classifier template
`templateECOC`	Error-correcting output codes learner template
`templateEnsemble`	Ensemble learning template
`templateGAM`	Generalized additive model (GAM) learner template (自 R2023b 起)
`templateGP`	Gaussian process template (自 R2023b 起)
`templateKernel`	Kernel learner template
`templateKNN`	k-nearest neighbor classifier template
`templateLinear`	Linear learner template
`templateNaiveBayes`	Naive Bayes classifier template
`templateSVM`	Support vector machine template
`templateTree`	Create decision tree template
`test`	Test indices for cross-validation
`test`	Test indices for time series cross-validation (自 R2022b 起)
`testcholdout`	Compare predictive accuracies of two classification models
`testckfold`	Compare accuracies of two classification models by repeated cross-validation
`testDeviance`	Deviance test for multinomial regression model (自 R2023a 起)
`tiedrank`	针对结值而调整的秩
`tinv`	Student t 逆累积分布函数
`tLocationScaleDistribution`	t 位置尺度概率分布对象
`tpdf`	Student t 概率密度函数
`training`	Training indices for cross-validation
`training`	Training indices for time series cross-validation (自 R2022b 起)
`transform`	Transform new data using generated features (自 R2021a 起)
`transform`	Transform new predictor data to remove disparate impact (自 R2022b 起)
`transform`	Transform predictors into extracted features
`TreeBagger`	Ensemble of bagged decision trees
`TriangularDistribution`	三角概率分布对象
`trimmean`	Mean, excluding outliers
`trnd`	Student's t random numbers
`truncate`	Truncate probability distribution object
`tsne`	t-Distributed Stochastic Neighbor Embedding
`tspartition`	Partition time series data for cross-validation (自 R2022b 起)
`tstat`	Student's t mean and variance
`ttest`	单样本和配对样本 t 检验
`ttest2`	双样本 t 检验
`tuneSampler`	Tune Hamiltonian Monte Carlo (HMC) sampler

U

`unidcdf`	Discrete uniform cumulative distribution function
`unidinv`	Discrete uniform inverse cumulative distribution function
`unidpdf`	Discrete uniform probability density function
`unidrnd`	来自离散均匀分布的随机数
`unidstat`	离散均匀均值和方差
`unifcdf`	连续均匀累积分布函数
`unifinv`	Continuous uniform inverse cumulative distribution function
`unifit`	Continuous uniform parameter estimates
`UniformDistribution`	均匀概率分布对象
`unifpdf`	Continuous uniform probability density function
`unifrnd`	连续均匀随机数
`unifstat`	连续均匀均值和方差
`union`	(Not Recommended) Set union for dataset array observations
`unique`	(Not Recommended) Unique observations in dataset array
`unstack`	(Not Recommended) Unstack dataset array from single variable into multiple variables
`update`	Update model parameters for code generation
`updateMetrics`	Update performance metrics in incremental drift-aware learning model given new data (自 R2022b 起)
`updateMetrics`	Update performance metrics in kernel incremental learning model given new data (自 R2022a 起)
`updateMetrics`	Update performance metrics in linear incremental learning model given new data (自 R2020b 起)
`updateMetrics`	Update performance metrics in ECOC incremental learning classification model given new data (自 R2022a 起)
`updateMetrics`	Update performance metrics in naive Bayes incremental learning classification model given new data (自 R2021a 起)
`updateMetricsAndFit`	Update performance metrics in incremental drift-aware learning model given new data and train model (自 R2022b 起)
`updateMetricsAndFit`	Update performance metrics in kernel incremental learning model given new data and train model (自 R2022a 起)
`updateMetricsAndFit`	Update performance metrics in linear incremental learning model given new data and train model (自 R2020b 起)
`updateMetricsAndFit`	Update performance metrics in ECOC incremental learning classification model given new data and train model (自 R2022a 起)
`updateMetricsAndFit`	Update performance metrics in naive Bayes incremental learning classification model given new data and train model (自 R2021a 起)
`upperparams`	Upper Pareto tail parameters

V

`validatedUpdateInputs`	Validate and extract machine learning model parameters to update
`var`	Variance of probability distribution
`varianceComponent`	Variance component estimates for analysis of variance (ANOVA) (自 R2022b 起)
`vartest`	Chi-square variance test
`vartest2`	Two-sample F-test for equal variances
`vartestn`	Multiple-sample tests for equal variances
`vertcat`	(Not Recommended) Vertical concatenation for dataset arrays
`view`	View regression tree
`view`	View classification tree

W

`wblcdf`	Weibull cumulative distribution function
`wblfit`	Weibull parameter estimates
`wblinv`	Weibull inverse cumulative distribution function
`wbllike`	Weibull negative log-likelihood
`wblpdf`	Weibull probability density function
`wblplot`	Weibull probability plot
`wblrnd`	威布尔随机数
`wblstat`	Weibull mean and variance
`WeibullDistribution`	威布尔概率分布对象
`wishrnd`	Wishart random numbers

X

`x2fx`	Convert predictor matrix to design matrix
`xptread`	Create table from data stored in SAS XPORT format file

Z

`zscore`	标准化 z 分数
`ztest`	z 检验

Statistics and Machine Learning Toolbox 函数 - 按字母顺序排列的列表

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Z

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