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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 |