Statistics and Machine Learning Toolbox. User's Guide

Getting Started
	Statistics and Machine Learning Toolbox Product Description
	Supported Data Types
Organizing Data
	Test Differences Between Category Means
	Grouping Variables
		What Are Grouping Variables?
		Group Definition
		Analysis Using Grouping Variables
		Missing Group Values
	Dummy Variables
		What Are Dummy Variables?
		Creating Dummy Variables
	Linear Regression with Categorical Covariates
Descriptive Statistics
	Measures of Central Tendency
		Measures of Central Tendency
	Measures of Dispersion
		Compare Measures of Dispersion
	Exploratory Analysis of Data
	Resampling Statistics
		Bootstrap Resampling
		Jackknife Resampling
		Parallel Computing Support for Resampling Methods
Statistical Visualization
	Create Scatter Plots Using Grouped Data
	Compare Grouped Data Using Box Plots
	Distribution Plots
		Normal Probability Plots
		Probability Plots
		Quantile-Quantile Plots
		Cumulative Distribution Plots
	Visualizing Multivariate Data
Probability Distributions
	Working with Probability Distributions
		Probability Distribution Objects
		Apps and Interactive User Interfaces
		Distribution-Specific Functions and Generic Distribution Functions
	Supported Distributions
		Continuous Distributions (Data)
		Continuous Distributions (Statistics)
		Discrete Distributions
		Multivariate Distributions
		Nonparametric Distributions
		Flexible Distribution Families
	Maximum Likelihood Estimation
	Negative Loglikelihood Functions
		Find MLEs Using Negative Loglikelihood Function
	Random Number Generation
	Nonparametric and Empirical Probability Distributions
		Overview
		Kernel Distribution
		Empirical Cumulative Distribution Function
		Piecewise Linear Distribution
		Pareto Tails
		Triangular Distribution
	Fit Kernel Distribution Object to Data
	Fit Kernel Distribution Using ksdensity
	Fit Distributions to Grouped Data Using ksdensity
	Fit a Nonparametric Distribution with Pareto Tails
	Generate Random Numbers Using the Triangular Distribution
	Model Data Using the Distribution Fitter App
		Explore Probability Distributions Interactively
		Create and Manage Data Sets
		Create a New Fit
		Display Results
		Manage Fits
		Evaluate Fits
		Exclude Data
		Save and Load Sessions
		Generate a File to Fit and Plot Distributions
	Fit a Distribution Using the Distribution Fitter App
		Step 1: Load Sample Data
		Step 2: Import Data
		Step 3: Create a New Fit
		Step 4: Create and Manage Additional Fits
	Define Custom Distributions Using the Distribution Fitter App
		Open the Distribution Fitter App
		Define Custom Distribution
		Import Custom Distribution
	Explore the Random Number Generation UI
	Compare Multiple Distribution Fits
	Fit Probability Distribution Objects to Grouped Data
	Three-Parameter Weibull Distribution
	Multinomial Probability Distribution Objects
	Multinomial Probability Distribution Functions
	Generate Random Numbers Using Uniform Distribution Inversion
	Represent Cauchy Distribution Using t Location-Scale
	Generate Cauchy Random Numbers Using Student's t
	Generate Correlated Data Using Rank Correlation
	Create Gaussian Mixture Model
	Fit Gaussian Mixture Model to Data
	Simulate Data from Gaussian Mixture Model
	Copulas: Generate Correlated Samples
		Determining Dependence Between Simulation Inputs
		Constructing Dependent Bivariate Distributions
		Using Rank Correlation Coefficients
		Using Bivariate Copulas
		Higher Dimension Copulas
		Archimedean Copulas
		Simulating Dependent Multivariate Data Using Copulas
		Fitting Copulas to Data
	Simulating Dependent Random Variables Using Copulas
	Fit Custom Distributions
	Avoid Numerical Issues When Fitting Custom Distributions
	Nonparametric Estimates of Cumulative Distribution Functions and Their Inverses
	Modelling Tail Data with the Generalized Pareto Distribution
	Modelling Data with the Generalized Extreme Value Distribution
	Curve Fitting and Distribution Fitting
	Fitting a Univariate Distribution Using Cumulative Probabilities
Gaussian Processes
	Gaussian Process Regression Models
		Compare Prediction Intervals of GPR Models
	Kernel (Covariance) Function Options
	Exact GPR Method
		Parameter Estimation
		Prediction
		Computational Complexity of Exact Parameter Estimation and Prediction
	Subset of Data Approximation for GPR Models
	Subset of Regressors Approximation for GPR Models
		Approximating the Kernel Function
		Parameter Estimation
		Prediction
		Predictive Variance Problem
	Fully Independent Conditional Approximation for GPR Models
		Approximating the Kernel Function
		Parameter Estimation
		Prediction
	Block Coordinate Descent Approximation for GPR Models
		Fit GPR Models Using BCD Approximation
	Predict Battery State of Charge Using Machine Learning
Random Number Generation
	Generating Pseudorandom Numbers
		Common Pseudorandom Number Generation Methods
	Representing Sampling Distributions Using Markov Chain Samplers
		Using the Metropolis-Hastings Algorithm
		Using Slice Sampling
		Using Hamiltonian Monte Carlo
	Generating Quasi-Random Numbers
		Quasi-Random Sequences
		Quasi-Random Point Sets
		Quasi-Random Streams
	Generating Data Using Flexible Families of Distributions
	Bayesian Linear Regression Using Hamiltonian Monte Carlo
	Bayesian Analysis for a Logistic Regression Model
Hypothesis Tests
	Hypothesis Test Terminology
	Hypothesis Test Assumptions
	Hypothesis Testing
	Available Hypothesis Tests
	Selecting a Sample Size
Analysis of Variance
	One-Way ANOVA
		Introduction to One-Way ANOVA
		Prepare Data for One-Way ANOVA
		Perform One-Way ANOVA
		Mathematical Details
	Two-Way ANOVA
		Introduction to Two-Way ANOVA
		Prepare Data for Balanced Two-Way ANOVA
		Perform Two-Way ANOVA
		Mathematical Details
	Multiple Comparisons
		Multiple Comparisons Using One-Way ANOVA
		Multiple Comparisons for Three-Way ANOVA
		Multiple Comparison Procedures
	N-Way ANOVA
		Introduction to N-Way ANOVA
		Prepare Data for N-Way ANOVA
		Perform N-Way ANOVA
	ANOVA with Random Effects
	Other ANOVA Models
	Analysis of Covariance
		Introduction to Analysis of Covariance
		Analysis of Covariance Tool
		Confidence Bounds
		Multiple Comparisons
	Nonparametric Methods
		Introduction to Nonparametric Methods
		Kruskal-Wallis Test
		Friedman's Test
	MANOVA
		Introduction to MANOVA
		ANOVA with Multiple Responses
	Model Specification for Repeated Measures Models
		Wilkinson Notation
	Compound Symmetry Assumption and Epsilon Corrections
	Mauchly’s Test of Sphericity
	Multivariate Analysis of Variance for Repeated Measures
Bayesian Optimization
	Bayesian Optimization Algorithm
		Algorithm Outline
		Gaussian Process Regression for Fitting the Model
		Acquisition Function Types
		Acquisition Function Maximization
	Parallel Bayesian Optimization
		Optimize in Parallel
		Parallel Bayesian Algorithm
		Settings for Best Parallel Performance
		Differences in Parallel Bayesian Optimization Output
	Bayesian Optimization Plot Functions
		Built-In Plot Functions
		Custom Plot Function Syntax
		Create a Custom Plot Function
	Bayesian Optimization Output Functions
		What Is a Bayesian Optimization Output Function?
		Built-In Output Functions
		Custom Output Functions
		Bayesian Optimization Output Function
	Bayesian Optimization Workflow
		What Is Bayesian Optimization?
		Ways to Perform Bayesian Optimization
		Bayesian Optimization Using a Fit Function
		Bayesian Optimization Using bayesopt
		Bayesian Optimization Characteristics
		Parameters Available for Fit Functions
		Hyperparameter Optimization Options for Fit Functions
	Variables for a Bayesian Optimization
		Syntax for Creating Optimization Variables
		Variables for Optimization Examples
	Bayesian Optimization Objective Functions
		Objective Function Syntax
		Objective Function Example
		Objective Function Errors
	Constraints in Bayesian Optimization
		Bounds
		Deterministic Constraints — XConstraintFcn
		Conditional Constraints — ConditionalVariableFcn
		Coupled Constraints
		Bayesian Optimization with Coupled Constraints
	Optimize Cross-Validated Classifier Using bayesopt
	Optimize Classifier Fit Using Bayesian Optimization
	Optimize a Boosted Regression Ensemble
Parametric Regression Analysis
	Choose a Regression Function
		Update Legacy Code with New Fitting Methods
	What Is a Linear Regression Model?
	Linear Regression
		Prepare Data
		Choose a Fitting Method
		Choose a Model or Range of Models
		Fit Model to Data
		Examine Quality and Adjust Fitted Model
		Predict or Simulate Responses to New Data
		Share Fitted Models
	Linear Regression Workflow
	Regression Using Dataset Arrays
	Linear Regression Using Tables
	Linear Regression with Interaction Effects
	Interpret Linear Regression Results
	Cook’s Distance
		Purpose
		Definition
		How To
		Determine Outliers Using Cook's Distance
	Coefficient Standard Errors and Confidence Intervals
		Coefficient Covariance and Standard Errors
		Coefficient Confidence Intervals
	Coefficient of Determination (R-Squared)
		Purpose
		Definition
		How To
		Display Coefficient of Determination
	Delete-1 Statistics
		Delete-1 Change in Covariance (CovRatio)
		Delete-1 Scaled Difference in Coefficient Estimates (Dfbetas)
		Delete-1 Scaled Change in Fitted Values (Dffits)
		Delete-1 Variance (S2_i)
	Durbin-Watson Test
		Purpose
		Definition
		How To
		Test for Autocorrelation Among Residuals
	F-statistic and t-statistic
		F-statistic
		Assess Fit of Model Using F-statistic
		t-statistic
		Assess Significance of Regression Coefficients Using t-statistic
	Hat Matrix and Leverage
		Hat Matrix
		Leverage
		Determine High Leverage Observations
	Residuals
		Purpose
		Definition
		How To
		Assess Model Assumptions Using Residuals
	Summary of Output and Diagnostic Statistics
	Wilkinson Notation
		Overview
		Formula Specification
		Linear Model Examples
		Linear Mixed-Effects Model Examples
		Generalized Linear Model Examples
		Generalized Linear Mixed-Effects Model Examples
		Repeated Measures Model Examples
	Stepwise Regression
		Stepwise Regression to Select Appropriate Models
		Compare large and small stepwise models
	Reduce Outlier Effects Using Robust Regression
		Why Use Robust Regression?
		Iteratively Reweighted Least Squares
		Compare Results of Standard and Robust Least-Squares Fit
		Steps for Iteratively Reweighted Least Squares
	Ridge Regression
		Introduction to Ridge Regression
		Ridge Regression
	Lasso and Elastic Net
		What Are Lasso and Elastic Net?
		Lasso and Elastic Net Details
		References
	Wide Data via Lasso and Parallel Computing
	Lasso Regularization
	Lasso and Elastic Net with Cross Validation
	Partial Least Squares
		Introduction to Partial Least Squares
		Perform Partial Least-Squares Regression
	Linear Mixed-Effects Models
	Prepare Data for Linear Mixed-Effects Models
		Tables and Dataset Arrays
		Design Matrices
		Relation of Matrix Form to Tables and Dataset Arrays
	Relationship Between Formula and Design Matrices
		Formula
		Design Matrices for Fixed and Random Effects
		Grouping Variables
	Estimating Parameters in Linear Mixed-Effects Models
		Maximum Likelihood (ML)
		Restricted Maximum Likelihood (REML)
	Linear Mixed-Effects Model Workflow
	Fit Mixed-Effects Spline Regression
	Train Linear Regression Model
	Analyze Time Series Data
	Partial Least Squares Regression and Principal Components Regression
	Accelerate Linear Model Fitting on GPU
Generalized Linear Models
	Multinomial Models for Nominal Responses
	Multinomial Models for Ordinal Responses
	Hierarchical Multinomial Models
	Generalized Linear Models
		What Are Generalized Linear Models?
		Prepare Data
		Choose Generalized Linear Model and Link Function
		Choose Fitting Method and Model
		Fit Model to Data
		Examine Quality and Adjust the Fitted Model
		Predict or Simulate Responses to New Data
		Share Fitted Models
	Generalized Linear Model Workflow
	Lasso Regularization of Generalized Linear Models
		What is Generalized Linear Model Lasso Regularization?
		Generalized Linear Model Lasso and Elastic Net
		References
	Regularize Poisson Regression
	Regularize Logistic Regression
	Regularize Wide Data in Parallel
	Generalized Linear Mixed-Effects Models
		What Are Generalized Linear Mixed-Effects Models?
		GLME Model Equations
		Prepare Data for Model Fitting
		Choose a Distribution Type for the Model
		Choose a Link Function for the Model
		Specify the Model Formula
		Display the Model
		Work with the Model
	Fit a Generalized Linear Mixed-Effects Model
	Fitting Data with Generalized Linear Models
	Train Generalized Additive Model for Binary Classification
	Train Generalized Additive Model for Regression
Nonlinear Regression
	Nonlinear Regression
		What Are Parametric Nonlinear Regression Models?
		Prepare Data
		Represent the Nonlinear Model
		Choose Initial Vector beta0
		Fit Nonlinear Model to Data
		Examine Quality and Adjust the Fitted Nonlinear Model
		Predict or Simulate Responses Using a Nonlinear Model
	Nonlinear Regression Workflow
	Mixed-Effects Models
		Introduction to Mixed-Effects Models
		Mixed-Effects Model Hierarchy
		Specifying Mixed-Effects Models
		Specifying Covariate Models
		Choosing nlmefit or nlmefitsa
		Using Output Functions with Mixed-Effects Models
	Examining Residuals for Model Verification
	Mixed-Effects Models Using nlmefit and nlmefitsa
	Weighted Nonlinear Regression
	Pitfalls in Fitting Nonlinear Models by Transforming to Linearity
	Nonlinear Logistic Regression
Time Series Forecasting
	Time Series Forecasting Using Ensemble of Boosted Regression Trees
Survival Analysis
	What Is Survival Analysis?
		Introduction
		Censoring
		Data
		Survivor Function
		Hazard Function
	Kaplan-Meier Method
	Hazard and Survivor Functions for Different Groups
	Survivor Functions for Two Groups
	Cox Proportional Hazards Model
		Introduction
		Hazard Ratio
		Extension of Cox Proportional Hazards Model
		Partial Likelihood Function
		Partial Likelihood Function for Tied Events
		Frequency or Weights of Observations
	Cox Proportional Hazards Model for Censored Data
	Cox Proportional Hazards Model with Time-Dependent Covariates
	Cox Proportional Hazards Model Object
	Analyzing Survival or Reliability Data
Multivariate Methods
	Multivariate Linear Regression
		Introduction to Multivariate Methods
		Multivariate Linear Regression Model
		Solving Multivariate Regression Problems
	Estimation of Multivariate Regression Models
		Least Squares Estimation
		Maximum Likelihood Estimation
		Missing Response Data
	Set Up Multivariate Regression Problems
		Response Matrix
		Design Matrices
		Common Multivariate Regression Problems
	Multivariate General Linear Model
	Fixed Effects Panel Model with Concurrent Correlation
	Longitudinal Analysis
	Multidimensional Scaling
	Nonclassical and Nonmetric Multidimensional Scaling
		Nonclassical Multidimensional Scaling
		Nonmetric Multidimensional Scaling
	Classical Multidimensional Scaling
	Compare Handwritten Shapes Using Procrustes Analysis
	Introduction to Feature Selection
		Feature Selection Algorithms
		Feature Selection Functions
	Sequential Feature Selection
		Introduction to Sequential Feature Selection
		Select Subset of Features with Comparative Predictive Power
	Nonnegative Matrix Factorization
	Perform Nonnegative Matrix Factorization
	Principal Component Analysis (PCA)
	Analyze Quality of Life in U.S. Cities Using PCA
	Factor Analysis
	Analyze Stock Prices Using Factor Analysis
	Robust Feature Selection Using NCA for Regression
	Neighborhood Component Analysis (NCA) Feature Selection
		NCA Feature Selection for Classification
		NCA Feature Selection for Regression
		Impact of Standardization
		Choosing the Regularization Parameter Value
	t-SNE
		What Is t-SNE?
		t-SNE Algorithm
		Barnes-Hut Variation of t-SNE
		Characteristics of t-SNE
	t-SNE Output Function
		t-SNE Output Function Description
		tsne optimValues Structure
		t-SNE Custom Output Function
	Visualize High-Dimensional Data Using t-SNE
	tsne Settings
	Feature Extraction
		What Is Feature Extraction?
		Sparse Filtering Algorithm
		Reconstruction ICA Algorithm
	Feature Extraction Workflow
	Extract Mixed Signals
	Select Features for Classifying High-Dimensional Data
	Perform Factor Analysis on Exam Grades
	Classical Multidimensional Scaling Applied to Nonspatial Distances
	Nonclassical Multidimensional Scaling
	Fitting an Orthogonal Regression Using Principal Components Analysis
	Tune Regularization Parameter to Detect Features Using NCA for Classification
Cluster Analysis
	Choose Cluster Analysis Method
		Clustering Methods
		Comparison of Clustering Methods
	Hierarchical Clustering
		Introduction to Hierarchical Clustering
		Algorithm Description
		Similarity Measures
		Linkages
		Dendrograms
		Verify the Cluster Tree
		Create Clusters
	DBSCAN
		Introduction to DBSCAN
		Algorithm Description
		Determine Values for DBSCAN Parameters
	Partition Data Using Spectral Clustering
		Introduction to Spectral Clustering
		Algorithm Description
		Estimate Number of Clusters and Perform Spectral Clustering
	k-Means Clustering
		Introduction to k-Means Clustering
		Compare k-Means Clustering Solutions
	Cluster Using Gaussian Mixture Model
		How Gaussian Mixture Models Cluster Data
		Fit GMM with Different Covariance Options and Initial Conditions
		When to Regularize
		Model Fit Statistics
	Cluster Gaussian Mixture Data Using Hard Clustering
	Cluster Gaussian Mixture Data Using Soft Clustering
	Tune Gaussian Mixture Models
	Cluster Evaluation
	Cluster Analysis
	Anomaly Detection with Isolation Forest
		Introduction to Isolation Forest
		Parameters for Isolation Forests
		Anomaly Scores
		Anomaly Indicators
		Detect Outliers and Plot Contours of Anomaly Scores
		Examine NumObservationsPerLearner for Small Data
	Unsupervised Anomaly Detection
		Outlier Detection
		Novelty Detection
	Model-Specific Anomaly Detection
		Detect Outliers After Training Random Forest
		Detect Outliers After Training Discriminant Analysis Classifier
Parametric Classification
	Parametric Classification
	ROC Curve and Performance Metrics
		Introduction to ROC Curve
		Performance Curve with MATLAB
		ROC Curve for Multiclass Classification
		Performance Metrics
		Classification Scores and Thresholds
		Pointwise Confidence Intervals
	Performance Curves by perfcurve
		Input Scores and Labels for perfcurve
		Computation of Performance Metrics
		Multiclass Classification Problems
		Confidence Intervals
		Observation Weights
	Classification
Nonparametric Supervised Learning
	Supervised Learning Workflow and Algorithms
		What Is Supervised Learning?
		Steps in Supervised Learning
		Characteristics of Classification Algorithms
		Misclassification Cost Matrix, Prior Probabilities, and Observation Weights
	Visualize Decision Surfaces of Different Classifiers
	Classification Using Nearest Neighbors
		Pairwise Distance Metrics
		k-Nearest Neighbor Search and Radius Search
		Classify Query Data
		Find Nearest Neighbors Using a Custom Distance Metric
		K-Nearest Neighbor Classification for Supervised Learning
		Construct KNN Classifier
		Examine Quality of KNN Classifier
		Predict Classification Using KNN Classifier
		Modify KNN Classifier
	Framework for Ensemble Learning
		Prepare the Predictor Data
		Prepare the Response Data
		Choose an Applicable Ensemble Aggregation Method
		Set the Number of Ensemble Members
		Prepare the Weak Learners
		Call fitcensemble or fitrensemble
	Ensemble Algorithms
		Bootstrap Aggregation (Bagging) and Random Forest
		Random Subspace
		Boosting Algorithms
	Train Classification Ensemble
	Train Regression Ensemble
	Select Predictors for Random Forests
	Test Ensemble Quality
	Ensemble Regularization
		Regularize a Regression Ensemble
	Classification with Imbalanced Data
	Handle Imbalanced Data or Unequal Misclassification Costs in Classification Ensembles
		Train Ensemble With Unequal Classification Costs
	Surrogate Splits
	LPBoost and TotalBoost for Small Ensembles
	Tune RobustBoost
	Random Subspace Classification
	Train Classification Ensemble in Parallel
	Bootstrap Aggregation (Bagging) of Regression Trees Using TreeBagger
	Bootstrap Aggregation (Bagging) of Classification Trees Using TreeBagger
	Detect Outliers Using Quantile Regression
	Conditional Quantile Estimation Using Kernel Smoothing
	Tune Random Forest Using Quantile Error and Bayesian Optimization
	Assess Neural Network Classifier Performance
	Assess Regression Neural Network Performance
	Automated Feature Engineering for Classification
		Interpret Linear Model with Generated Features
		Generate New Features to Improve Bagged Ensemble Accuracy
	Automated Feature Engineering for Regression
		Interpret Linear Model with Generated Features
		Generate New Features to Improve Bagged Ensemble Performance
	Moving Towards Automating Model Selection Using Bayesian Optimization
	Automated Classifier Selection with Bayesian and ASHA Optimization
	Automated Regression Model Selection with Bayesian and ASHA Optimization
	Credit Rating by Bagging Decision Trees
	Combine Heterogeneous Models into Stacked Ensemble
	Label Data Using Semi-Supervised Learning Techniques
	Bibliography
Decision Trees
	Decision Trees
		Train Classification Tree
		Train Regression Tree
	View Decision Tree
	Growing Decision Trees
	Prediction Using Classification and Regression Trees
	Predict Out-of-Sample Responses of Subtrees
	Improving Classification Trees and Regression Trees
		Examining Resubstitution Error
		Cross Validation
		Choose Split Predictor Selection Technique
		Control Depth or “Leafiness”
		Pruning
	Splitting Categorical Predictors in Classification Trees
		Challenges in Splitting Multilevel Predictors
		Algorithms for Categorical Predictor Split
		Inspect Data with Multilevel Categorical Predictors
Discriminant Analysis
	Discriminant Analysis Classification
		Create Discriminant Analysis Classifiers
	Creating Discriminant Analysis Model
		Weighted Observations
	Prediction Using Discriminant Analysis Models
		Posterior Probability
		Prior Probability
		Cost
	Create and Visualize Discriminant Analysis Classifier
	Improving Discriminant Analysis Models
		Deal with Singular Data
		Choose a Discriminant Type
		Examine the Resubstitution Error and Confusion Matrix
		Cross Validation
		Change Costs and Priors
	Regularize Discriminant Analysis Classifier
	Examine the Gaussian Mixture Assumption
		Bartlett Test of Equal Covariance Matrices for Linear Discriminant Analysis
		Q-Q Plot
		Mardia Kurtosis Test of Multivariate Normality
Naive Bayes
	Naive Bayes Classification
		Supported Distributions
	Plot Posterior Classification Probabilities
Classification Learner
	Machine Learning in MATLAB
		What Is Machine Learning?
		Selecting the Right Algorithm
		Train Classification Models in Classification Learner App
		Train Regression Models in Regression Learner App
		Train Neural Networks for Deep Learning
	Train Classification Models in Classification Learner App
		Automated Classifier Training
		Manual Classifier Training
		Parallel Classifier Training
		Compare and Improve Classification Models
	Select Data for Classification or Open Saved App Session
		Select Data from Workspace
		Import Data from File
		Example Data for Classification
		Choose Validation Scheme
		(Optional) Reserve Data for Testing
		Save and Open App Session
	Choose Classifier Options
		Choose Classifier Type
		Decision Trees
		Discriminant Analysis
		Logistic Regression Classifiers
		Naive Bayes Classifiers
		Support Vector Machines
		Efficiently Trained Linear Classifiers
		Nearest Neighbor Classifiers
		Kernel Approximation Classifiers
		Ensemble Classifiers
		Neural Network Classifiers
	Feature Selection and Feature Transformation Using Classification Learner App
		Investigate Features in the Scatter Plot
		Select Features to Include
		Transform Features with PCA in Classification Learner
		Investigate Features in the Parallel Coordinates Plot
	Misclassification Costs in Classification Learner App
		Specify Misclassification Costs
		Assess Model Performance
		Misclassification Costs in Exported Model and Generated Code
	Hyperparameter Optimization in Classification Learner App
		Select Hyperparameters to Optimize
		Optimization Options
		Minimum Classification Error Plot
		Optimization Results
	Visualize and Assess Classifier Performance in Classification Learner
		Check Performance in the Models Pane
		View Model Metrics in Summary Tab and Models Pane
		Compare Model Information and Results in Table View
		Plot Classifier Results
		Check Performance Per Class in the Confusion Matrix
		Check ROC Curve
		Interpret Model Using Partial Dependence Plots
		Compare Model Plots by Changing Layout
		Evaluate Test Set Model Performance
	Export Plots in Classification Learner App
	Export Classification Model to Predict New Data
		Export the Model to the Workspace to Make Predictions for New Data
		Make Predictions for New Data Using Exported Model
		Generate MATLAB Code to Train the Model with New Data
		Generate C Code for Prediction
		Deploy Predictions Using MATLAB Compiler
		Export Model for Deployment to MATLAB Production Server
	Train Decision Trees Using Classification Learner App
	Train Discriminant Analysis Classifiers Using Classification Learner App
	Train Binary GLM Logistic Regression Classifier Using Classification Learner App
	Train Support Vector Machines Using Classification Learner App
	Train Nearest Neighbor Classifiers Using Classification Learner App
	Train Kernel Approximation Classifiers Using Classification Learner App
	Train Ensemble Classifiers Using Classification Learner App
	Train Naive Bayes Classifiers Using Classification Learner App
	Train Neural Network Classifiers Using Classification Learner App
	Train and Compare Classifiers Using Misclassification Costs in Classification Learner App
	Train Classifier Using Hyperparameter Optimization in Classification Learner App
	Check Classifier Performance Using Test Set in Classification Learner App
	Interpret Classifiers Trained in Classification Learner App
	Deploy Model Trained in Classification Learner to MATLAB Production Server
		Choose Trained Model to Deploy
		Export Model for Deployment
		(Optional) Simulate Model Deployment
		Package Code
	Build Condition Model for Industrial Machinery and Manufacturing Processes
		Load Data
		Import Data into App and Partition Data
		Train Models Using All Features
		Assess Model Performance
		Export Model to the Workspace and Save App Session
		Check Model Size
		Resume App Session
		Select Features Using Feature Ranking
		Investigate Important Features in Scatter Plot
		Further Experimentation
		Assess Model Accuracy on Test Set
		Export Final Model
	Export Model from Classification Learner to Experiment Manager
		Export Classification Model
		Select Hyperparameters
		(Optional) Customize Experiment
		Run Experiment
	Tune Classification Model Using Experiment Manager
		Load and Partition Data
		Train Models in Classification Learner
		Assess Best Model Performance
		Export Model to Experiment Manager
		Run Experiment with Default Hyperparameters
		Adjust Hyperparameters and Hyperparameter Values
		Specify Training Data
		Customize Confusion Matrix
		Export and Use Final Model
Regression Learner
	Train Regression Models in Regression Learner App
		Automated Regression Model Training
		Manual Regression Model Training
		Parallel Regression Model Training
		Compare and Improve Regression Models
	Select Data for Regression or Open Saved App Session
		Select Data from Workspace
		Import Data from File
		Example Data for Regression
		Choose Validation Scheme
		(Optional) Reserve Data for Testing
		Save and Open App Session
	Choose Regression Model Options
		Choose Regression Model Type
		Linear Regression Models
		Regression Trees
		Support Vector Machines
		Gaussian Process Regression Models
		Kernel Approximation Models
		Ensembles of Trees
		Neural Networks
	Feature Selection and Feature Transformation Using Regression Learner App
		Investigate Features in the Response Plot
		Select Features to Include
		Transform Features with PCA in Regression Learner
	Hyperparameter Optimization in Regression Learner App
		Select Hyperparameters to Optimize
		Optimization Options
		Minimum MSE Plot
		Optimization Results
	Visualize and Assess Model Performance in Regression Learner
		Check Performance in Models Pane
		View Model Metrics in Summary Tab and Models Pane
		Compare Model Information and Results in Table View
		Explore Data and Results in Response Plot
		Plot Predicted vs. Actual Response
		Evaluate Model Using Residuals Plot
		Interpret Model Using Partial Dependence Plots
		Compare Model Plots by Changing Layout
		Evaluate Test Set Model Performance
	Export Plots in Regression Learner App
	Export Regression Model to Predict New Data
		Export Model to Workspace
		Make Predictions for New Data Using Exported Model
		Generate MATLAB Code to Train Model with New Data
		Generate C Code for Prediction
		Deploy Predictions Using MATLAB Compiler
		Export Model for Deployment to MATLAB Production Server
	Train Regression Trees Using Regression Learner App
	Train Regression Neural Networks Using Regression Learner App
	Train Kernel Approximation Model Using Regression Learner App
	Train Regression Model Using Hyperparameter Optimization in Regression Learner App
	Check Model Performance Using Test Set in Regression Learner App
	Interpret Regression Models Trained in Regression Learner App
	Deploy Model Trained in Regression Learner to MATLAB Production Server
		Choose Trained Model to Deploy
		Export Model for Deployment
		(Optional) Simulate Model Deployment
		Package Code
	Export Model from Regression Learner to Experiment Manager
		Export Regression Model
		Select Hyperparameters
		(Optional) Customize Experiment
		Run Experiment
	Tune Regression Model Using Experiment Manager
		Load and Partition Data
		Train Models in Regression Learner
		Assess Best Model Performance
		Export Model to Experiment Manager
		Run Experiment with Default Hyperparameters
		Adjust Hyperparameters and Hyperparameter Values
		Specify Training Data
		Add Residuals Plot
		Export and Use Final Model
Support Vector Machines
	Support Vector Machines for Binary Classification
		Understanding Support Vector Machines
		Using Support Vector Machines
		Train SVM Classifiers Using a Gaussian Kernel
		Train SVM Classifier Using Custom Kernel
		Optimize Classifier Fit Using Bayesian Optimization
		Plot Posterior Probability Regions for SVM Classification Models
		Analyze Images Using Linear Support Vector Machines
	Understanding Support Vector Machine Regression
		Mathematical Formulation of SVM Regression
		Solving the SVM Regression Optimization Problem
Fairness
	Introduction to Fairness in Binary Classification
		Reduce Statistical Parity Difference Using Fairness Weights
		Reduce Disparate Impact of Predictions
Interpretability
	Interpret Machine Learning Models
		Features for Model Interpretation
		Interpret Classification Model
		Interpret Regression Model
	Shapley Values for Machine Learning Model
		What Is a Shapley Value?
		Shapley Value in Statistics and Machine Learning Toolbox
		Algorithms
		Specify Computation Algorithm
		Computational Cost
		Reduce Computational Cost
Incremental Learning
	Incremental Learning Overview
		What Is Incremental Learning?
		Incremental Learning with MATLAB
	Configure Incremental Learning Model
		Call Object Directly
		Convert Traditionally Trained Model
	Implement Incremental Learning for Regression Using Succinct Workflow
	Implement Incremental Learning for Classification Using Succinct Workflow
	Implement Incremental Learning for Regression Using Flexible Workflow
	Implement Incremental Learning for Classification Using Flexible Workflow
	Initialize Incremental Learning Model from SVM Regression Model Trained in Regression Learner
	Initialize Incremental Learning Model from Logistic Regression Model Trained in Classification Learner
	Perform Conditional Training During Incremental Learning
	Perform Text Classification Incrementally
	Incremental Learning with Naive Bayes and Heterogeneous Data
	Monitor Equipment State of Health Using Drift-Aware Learning
Markov Models
	Markov Chains
	Hidden Markov Models (HMM)
		Introduction to Hidden Markov Models (HMM)
		Analyzing Hidden Markov Models
Design of Experiments
	Design of Experiments
	Full Factorial Designs
		Multilevel Designs
		Two-Level Designs
	Fractional Factorial Designs
		Introduction to Fractional Factorial Designs
		Plackett-Burman Designs
		General Fractional Designs
	Response Surface Designs
		Introduction to Response Surface Designs
		Central Composite Designs
		Box-Behnken Designs
	D-Optimal Designs
		Introduction to D-Optimal Designs
		Generate D-Optimal Designs
		Augment D-Optimal Designs
		Specify Fixed Covariate Factors
		Specify Categorical Factors
		Specify Candidate Sets
	Improve an Engine Cooling Fan Using Design for Six Sigma Techniques
Statistical Process Control
	Control Charts
	Capability Studies
Tall Arrays
	Logistic Regression with Tall Arrays
	Bayesian Optimization with Tall Arrays
	Statistics and Machine Learning with Big Data Using Tall Arrays
Parallel Statistics
	Quick Start Parallel Computing for Statistics and Machine Learning Toolbox
		Parallel Statistics and Machine Learning Toolbox Functionality
		How to Compute in Parallel
	Use Parallel Processing for Regression TreeBagger Workflow
	Concepts of Parallel Computing in Statistics and Machine Learning Toolbox
		Subtleties in Parallel Computing
		Vocabulary for Parallel Computation
	When to Run Statistical Functions in Parallel
		Why Run in Parallel?
		Factors Affecting Speed
		Factors Affecting Results
	Analyze and Model Data on GPU
	Working with parfor
		How Statistical Functions Use parfor
		Characteristics of parfor
	Reproducibility in Parallel Statistical Computations
		Issues and Considerations in Reproducing Parallel Computations
		Running Reproducible Parallel Computations
		Parallel Statistical Computation Using Random Numbers
	Implement Jackknife Using Parallel Computing
	Implement Cross-Validation Using Parallel Computing
		Simple Parallel Cross Validation
		Reproducible Parallel Cross Validation
	Implement Bootstrap Using Parallel Computing
		Bootstrap in Serial and Parallel
		Reproducible Parallel Bootstrap
Code Generation
	Introduction to Code Generation
		Code Generation Workflows
		Code Generation Applications
	General Code Generation Workflow
		Define Entry-Point Function
		Generate Code
		Verify Generated Code
	Code Generation for Prediction of Machine Learning Model at Command Line
	Code Generation for Incremental Learning
	Code Generation for Nearest Neighbor Searcher
	Code Generation for Prediction of Machine Learning Model Using MATLAB Coder App
	Code Generation and Classification Learner App
		Load Sample Data
		Enable PCA
		Train Models
		Export Model to Workspace
		Generate C Code for Prediction
	Deploy Neural Network Regression Model to FPGA/ASIC Platform
	Predict Class Labels Using MATLAB Function Block
	Specify Variable-Size Arguments for Code Generation
	Create Dummy Variables for Categorical Predictors and Generate C/C++ Code
	System Objects for Classification and Code Generation
	Predict Class Labels Using Stateflow
	Human Activity Recognition Simulink Model for Smartphone Deployment
	Human Activity Recognition Simulink Model for Fixed-Point Deployment
	Code Generation for Prediction and Update Using Coder Configurer
	Code Generation for Probability Distribution Objects
	Fixed-Point Code Generation for Prediction of SVM
	Generate Code to Classify Data in Table
	Code Generation for Image Classification
	Predict Class Labels Using ClassificationSVM Predict Block
	Predict Responses Using RegressionSVM Predict Block
	Predict Class Labels Using ClassificationTree Predict Block
	Predict Responses Using RegressionTree Predict Block
	Predict Class Labels Using ClassificationEnsemble Predict Block
	Predict Responses Using RegressionEnsemble Predict Block
	Predict Class Labels Using ClassificationNeuralNetwork Predict Block
	Predict Responses Using RegressionNeuralNetwork Predict Block
	Predict Responses Using RegressionGP Predict Block
	Predict Class Labels Using ClassificationKNN Predict Block
	Predict Class Labels Using ClassificationLinear Predict Block
	Predict Responses Using RegressionLinear Predict Block
	Predict Class Labels Using ClassificationECOC Predict Block
	Code Generation for Binary GLM Logistic Regression Model Trained in Classification Learner
	Code Generation for Anomaly Detection
	Compress Machine Learning Model for Memory-Limited Hardware
	Verify and Validate Machine Learning Models Using Model-Based Design
Functions
	addedvarplot
	addK
	addlevels
	addInteractions
	qrandstream.addlistener
	addMetrics
	GeneralizedLinearMixedModel.anova
	addTerms
	addTerms
	adtest
	andrewsplot
	anova
	LinearMixedModel.anova
	anova
	anova1
	anova2
	anovan
	anova
	ansaribradley
	aoctool
	TreeBagger.append
	average
	barttest
	BayesianOptimization
	bayesopt
	bbdesign
	bestPoint
	betacdf
	betafit
	betainv
	betalike
	betapdf
	betarnd
	betastat
	binocdf
	binofit
	binoinv
	binopdf
	binornd
	binostat
	binScatterPlot
	biplot
	bootci
	bootstrp
	boxchart
	boxplot
	boundary
	CalinskiHarabaszEvaluation
	candexch
	candgen
	canoncorr
	capability
	capaplot
	caseread
	casewrite
	DaviesBouldinEvaluation
	dataset.cat
	cdf
	ccdesign
	cdf
	cdfplot
	cell2dataset
	dataset.cellstr
	chi2cdf
	chi2gof
	chi2inv
	chi2pdf
	chi2rnd
	chi2stat
	cholcov
	ClassificationBaggedEnsemble
	ClassificationECOC
	ClassificationECOC Predict
	ClassificationECOCCoderConfigurer
	ClassificationDiscriminant
	ClassificationEnsemble
	ClassificationEnsemble Predict
	ClassificationKNN
	ClassificationKNN Predict
	ClassificationLinear
	ClassificationLinear Predict
	ClassificationLinearCoderConfigurer
	ClassificationNaiveBayes
	ClassificationNeuralNetwork
	ClassificationNeuralNetwork Predict
	ClassificationPartitionedECOC
	ClassificationPartitionedEnsemble
	ClassificationPartitionedGAM
	ClassificationPartitionedKernel
	ClassificationPartitionedKernelECOC
	ClassificationPartitionedLinear
	ClassificationPartitionedLinearECOC
	ClassificationPartitionedModel
	ClassificationSVM
	ClassificationSVMCoderConfigurer
	ClassificationSVM Predict
	ClassificationTree
	ClassificationTree Predict
	ClassificationTreeCoderConfigurer
	classify
	cluster
	cluster
	clusterdata
	Cluster Data
	cmdscale
	coefci
	coefCI
	GeneralizedLinearMixedModel.coefCI
	coefCI
	LinearMixedModel.coefCI
	coefCI
	coefCI
	coefTest
	GeneralizedLinearMixedModel.coefTest
	coefTest
	LinearMixedModel.coefTest
	coefTest
	coefTest
	RepeatedMeasuresModel.coeftest
	CompactTreeBagger.combine
	combnk
	compact
	ClassificationDiscriminant.compact
	compact
	ClassificationEnsemble.compact
	ClassificationTree.compact
	compact
	compact
	RegressionEnsemble.compact
	RegressionSVM.compact
	RegressionTree.compact
	TreeBagger.compact
	CompactClassificationDiscriminant
	CompactClassificationECOC
	CompactClassificationEnsemble
	ClassificationGAM
	CompactClassificationNaiveBayes
	CompactClassificationNeuralNetwork
	CompactClassificationGAM
	CompactClassificationSVM
	CompactClassificationTree
	CompactLinearModel
	CompactGeneralizedLinearModel
	CompactRegressionEnsemble
	CompactRegressionGAM
	CompactRegressionGP
	CompactRegressionNeuralNetwork
	CompactRegressionSVM
	CompactRegressionTree
	CompactTreeBagger
	GeneralizedLinearMixedModel.compare
	LinearMixedModel.compare
	compareHoldout
	confusionchart
	ConfusionMatrixChart
	confusionmat
	controlchart
	controlrules
	cophenet
	copulacdf
	copulafit
	copulaparam
	copulapdf
	copulastat
	copularnd
	cordexch
	corr
	corrcov
	GeneralizedLinearMixedModel.covarianceParameters
	LinearMixedModel.covarianceParameters
	CoxModel
	coxphfit
	createns
	crosstab
	crossval
	crossval
	ClassificationDiscriminant.crossval
	crossval
	ClassificationEnsemble.crossval
	ClassificationTree.crossval
	RegressionEnsemble.crossval
	RegressionSVM.crossval
	RegressionTree.crossval
	ClassificationTree.cvloss
	RegressionTree.cvloss
	cvpartition
	ClassificationDiscriminant.cvshrink
	RegressionEnsemble.cvshrink
	datasample
	dataset
	dataset
	dataset.dataset2cell
	dataset.dataset2struct
	dataset2table
	dataset.datasetfun
	daugment
	dbscan
	dcovary
	qrandstream.delete
	dendrogram
	describe
	designecoc
	detectdrift
	detectdrift
	devianceTest
	GeneralizedLinearMixedModel.designMatrix
	LinearMixedModel.designMatrix
	discardResiduals
	discardSupportVectors
	discardSupportVectors
	CompactRegressionSVM.discardSupportVectors
	dataset.disp
	GeneralizedLinearMixedModel.disp
	LinearMixedModel.disp
	qrandstream.disp
	disparateImpactRemover
	dataset.display
	distributionFitter
	Probability Distribution Function
	dataset.double
	DriftDiagnostics
	ecdf
	histcounts
	plotDriftStatus
	plotEmpiricalCDF
	plotHistogram
	plotPermutationResults
	summary
	DriftDetectionMethod
	droplevels
	dummyvar
	dwtest
	dwtest
	ecdf
	ecdfhist
	edge
	edge
	ClassificationLinear.edge
	CompactClassificationDiscriminant.edge
	edge
	CompactClassificationEnsemble.edge
	edge
	edge
	edge
	CompactClassificationTree.edge
	dataset.end
	RepeatedMeasuresModel.epsilon
	evcdf
	evfit
	evinv
	qrandstream.eq
	CompactTreeBagger.error
	TreeBagger.error
	evalclusters
	evlike
	evpdf
	evrnd
	evstat
	expcdf
	expfit
	ExhaustiveSearcher
	expinv
	explike
	dataset.export
	exppdf
	exprnd
	expstat
	factoran
	fairnessMetrics
	fairnessThresholder
	fairnessWeights
	fcdf
	FeatureTransformer
	feval
	feval
	feval
	feval
	ff2n
	TreeBagger.fillprox
	qrandstream.findobj
	qrandstream.findprop
	finv
	fishertest
	fit
	fit
	fit
	fit
	fit
	fit
	fit
	fitcauto
	fitcdiscr
	fitcecoc
	fitcensemble
	fitcgam
	fitcknn
	fitclinear
	fitcnb
	fitcnet
	fitcox
	fitcsvm
	fitctree
	fitglm
	fitglme
	fitgmdist
	fitlm
	fitlme
	fitlmematrix
	fitmnr
	fitsemigraph
	fitsemiself
	fitrauto
	fitrgam
	fitrgp
	fitrlinear
	fitrm
	fitrnet
	fitdist
	fitensemble
	fitnlm
	fitPosterior
	fitPosterior
	fitrensemble
	fitrsvm
	fitrtree
	fitSVMPosterior
	GeneralizedLinearMixedModel.fitted
	LinearMixedModel.fitted
	GeneralizedLinearMixedModel.fixedEffects
	LinearMixedModel.fixedEffects
	fpdf
	fracfact
	fracfactgen
	friedman
	frnd
	fscchi2
	fscmrmr
	fscnca
	fsrnca
	fsrftest
	fsrmrmr
	fstat
	fsulaplacian
	fsurfht
	fullfact
	gagerr
	gamcdf
	gamfit
	gaminv
	gamlike
	gampdf
	gamrnd
	gamstat
	gardnerAltmanPlot
	gather
	qrandstream.ge
	GeneralizedLinearMixedModel
	GeneralizedLinearModel
	generateCode
	generateFiles
	generateLearnerDataTypeFcn
	gencfeatures
	genrfeatures
	geocdf
	geoinv
	geomean
	geopdf
	geornd
	geostat
	GapEvaluation
	dataset.get
	getlabels
	getlevels
	gevcdf
	gevfit
	gevinv
	gevlike
	gevpdf
	gevrnd
	gevstat
	gline
	glmfit
	glmval
	glyphplot
	gmdistribution
	gname
	gpcdf
	gpfit
	gpinv
	gplike
	gppdf
	gplotmatrix
	gprnd
	gpstat
	groupmeans
	TreeBagger.growTrees
	grp2idx
	grpstats
	RepeatedMeasuresModel.grpstats
	gscatter
	qrandstream.gt
	haltonset
	harmmean
	hazardratio
	hist3
	histfit
	hmmdecode
	hmmestimate
	hmmgenerate
	hmmtrain
	hmmviterbi
	HoeffdingDriftDetectionMethod
	dataset.horzcat
	hougen
	hygecdf
	hygeinv
	hygepdf
	hygernd
	hygestat
	hyperparameters
	icdf
	inconsistent
	increaseB
	interactionplot
	dataset.intersect
	invpred
	iqr
	incrementalConceptDriftDetector
	incrementalClassificationECOC
	incrementalClassificationKernel
	incrementalClassificationLinear
	incrementalClassificationNaiveBayes
	incrementalDriftAwareLearner
	incrementalLearner
	incrementalLearner
	incrementalLearner
	incrementalLearner
	incrementalLearner
	incrementalLearner
	incrementalLearner
	incrementalLearner
	incrementalRegressionKernel
	incrementalRegressionLinear
	dataset.isempty
	isanomaly
	isanomaly
	isanomaly
	isanomaly
	islevel
	iforest
	dataset.ismember
	dataset.ismissing
	IsolationForest
	qrandstream.isvalid
	iwishrnd
	jackknife
	jbtest
	johnsrnd
	dataset.join
	KDTreeSearcher
	kfoldEdge
	kfoldEdge
	kfoldEdge
	ClassificationPartitionedLinear.kfoldEdge
	ClassificationPartitionedLinearECOC.kfoldEdge
	kfoldEdge
	kfoldfun
	kfoldfun
	kfoldfun
	kfoldLoss
	kfoldLoss
	kfoldLoss
	ClassificationPartitionedLinear.kfoldLoss
	ClassificationPartitionedLinearECOC.kfoldLoss
	kfoldLoss
	RegressionPartitionedLinear.kfoldLoss
	kfoldLoss
	kfoldMargin
	kfoldMargin
	kfoldMargin
	ClassificationPartitionedLinear.kfoldMargin
	ClassificationPartitionedLinearECOC.kfoldMargin
	kfoldMargin
	kfoldPredict
	kfoldPredict
	kfoldPredict
	ClassificationPartitionedLinear.kfoldPredict
	ClassificationPartitionedLinearECOC.kfoldPredict
	kfoldPredict
	RegressionPartitionedLinear.kfoldPredict
	kfoldPredict
	kmeans
	kmedoids
	knnsearch
	knnsearch
	kruskalwallis
	ksdensity
	kstest
	kstest2
	kurtosis
	lasso
	lassoglm
	lassoPlot
	qrandstream.le
	learnerCoderConfigurer
	dataset.length
	levelcounts
	leverage
	lhsdesign
	lhsnorm
	lillietest
	lime
	LinearModel
	LinearMixedModel
	linhyptest
	linhyptest
	linkage
	loadCompactModel
	loadLearnerForCoder
	LocalOutlierFactor
	lof
	logncdf
	lognfit
	logninv
	lognlike
	lognpdf
	lognrnd
	lognstat
	CompactClassificationDiscriminant.logp
	logp
	logp
	loss
	loss
	ClassificationLinear.loss
	CompactClassificationDiscriminant.loss
	loss
	CompactClassificationEnsemble.loss
	loss
	loss
	loss
	CompactClassificationTree.loss
	CompactRegressionEnsemble.loss
	CompactRegressionGP.loss
	loss
	CompactRegressionSVM.loss
	CompactRegressionTree.loss
	loss
	loss
	loss
	loss
	loss
	loss
	FeatureSelectionNCAClassification.loss
	FeatureSelectionNCARegression.loss
	loss
	RegressionLinear.loss
	lowerparams
	qrandstream.lt
	lsline
	mad
	mahal
	CompactClassificationDiscriminant.mahal
	mahal
	maineffectsplot
	makecdiscr
	makedist
	RepeatedMeasuresModel.manova
	manova1
	manovacluster
	margin
	margin
	ClassificationLinear.margin
	CompactClassificationDiscriminant.margin
	margin
	CompactClassificationEnsemble.margin
	margin
	margin
	margin
	CompactClassificationTree.margin
	CompactTreeBagger.margin
	TreeBagger.margin
	RepeatedMeasuresModel.margmean
	RepeatedMeasuresModel.mauchly
	mat2dataset
	mdscale
	CompactTreeBagger.mdsprox
	TreeBagger.mdsprox
	mean
	meanEffectSize
	CompactTreeBagger.meanMargin
	TreeBagger.meanMargin
	CompactClassificationTree.surrogateAssociation
	CompactRegressionTree.surrogateAssociation
	median
	mergelevels
	mhsample
	mle
	mlecov
	mnpdf
	mnrfit
	mnrnd
	mnrval
	moment
	multcompare
	MultinomialRegression
	multcompare
	RepeatedMeasuresModel.multcompare
	multivarichart
	mvksdensity
	mvncdf
	mvnpdf
	mvregress
	mvregresslike
	mvnrnd
	mvtcdf
	mvtpdf
	mvtrnd
	nancov
	nanmax
	nanmean
	nanmedian
	nanmin
	nanstd
	nansum
	nanvar
	nearcorr
	nbincdf
	nbinfit
	nbininv
	nbinpdf
	nbinrnd
	nbinstat
	FeatureSelectionNCAClassification
	FeatureSelectionNCARegression
	ncfcdf
	ncfinv
	ncfpdf
	ncfrnd
	ncfstat
	nctcdf
	nctinv
	nctpdf
	nctrnd
	nctstat
	ncx2cdf
	ncx2inv
	ncx2pdf
	ncx2rnd
	ncx2stat
	dataset.ndims
	qrandstream.ne
	negloglik
	net
	CompactClassificationDiscriminant.nLinearCoeffs
	nlinfit
	nlintool
	nlmefit
	nlmefitsa
	nlparci
	nlpredci
	nnmf
	nodeVariableRange
	nominal
	qrandstream.notify
	NonLinearModel
	normcdf
	normfit
	norminv
	normlike
	normpdf
	normplot
	normrnd
	normspec
	normstat
	nsegments
	dataset.numel
	ocsvm
	OneClassSVM
	onehotdecode
	onehotencode
	optimalleaforder
	ClassificationBaggedEnsemble.oobEdge
	TreeBagger.oobError
	ClassificationBaggedEnsemble.oobLoss
	RegressionBaggedEnsemble.oobLoss
	ClassificationBaggedEnsemble.oobMargin
	TreeBagger.oobMargin
	TreeBagger.oobMeanMargin
	ClassificationBaggedEnsemble.oobPermutedPredictorImportance
	RegressionBaggedEnsemble.oobPermutedPredictorImportance
	ClassificationBaggedEnsemble.oobPredict
	RegressionBaggedEnsemble.oobPredict
	TreeBagger.oobPredict
	TreeBagger.oobQuantileError
	TreeBagger.oobQuantilePredict
	optimizableVariable
	ordinal
	CompactTreeBagger.outlierMeasure
	parallelcoords
	paramci
	paretotails
	partialcorr
	partialcorri
	partialDependence
	pca
	pcacov
	perObservationLoss
	perObservationLoss
	perObservationLoss
	pcares
	ppca
	pdf
	pdf
	pdist
	pdist2
	pearsrnd
	perfcurve
	plot
	plot
	plot
	plot
	plot
	RepeatedMeasuresModel.plot
	plot
	plot
	plotAdded
	plotAdjustedResponse
	plot
	plotComparisons
	plotDiagnostics
	plotDiagnostics
	plotDiagnostics
	plotEffects
	plotInteraction
	plotLocalEffects
	plotPartialDependence
	RepeatedMeasuresModel.plotprofile
	plotResiduals
	GeneralizedLinearMixedModel.plotResiduals
	plotResiduals
	LinearMixedModel.plotResiduals
	plotResiduals
	plotResiduals
	plotSlice
	plotSlice
	plotSlice
	plotSlice
	plotSurvival
	plsregress
	qrandstream.PointSet
	poisscdf
	poissfit
	poissinv
	poisspdf
	poissrnd
	poisstat
	polyconf
	polytool
	posterior
	RegressionGP.postFitStatistics
	predict
	predict
	ClassificationLinear.predict
	CompactClassificationDiscriminant.predict
	predict
	CompactClassificationEnsemble.predict
	predict
	predict
	predict
	predict
	CompactRegressionEnsemble.predict
	CompactRegressionGP.predict
	predict
	CompactRegressionSVM.predict
	CompactRegressionTree.predict
	predict
	predict
	predict
	predict
	predict
	predict
	predict
	RegressionLinear.predict
	CompactTreeBagger.predict
	predict
	GeneralizedLinearMixedModel.predict
	predict
	LinearMixedModel.predict
	predict
	FeatureSelectionNCAClassification.predict
	FeatureSelectionNCARegression.predict
	predict
	RepeatedMeasuresModel.predict
	predict
	predict
	TreeBagger.predict
	predictConstraints
	predictError
	predictObjective
	predictObjectiveEvaluationTime
	CompactClassificationEnsemble.predictorImportance
	CompactClassificationTree.predictorImportance
	CompactRegressionEnsemble.predictorImportance
	CompactRegressionTree.predictorImportance
	probplot
	procrustes
	proflik
	CompactTreeBagger.proximity
	ClassificationTree.prune
	RegressionTree.prune
	qrandstream.qrand
	qrandstream
	qrandstream
	qqplot
	qrandstream.rand
	TreeBagger.quantileError
	TreeBagger.quantilePredict
	randg
	random
	random
	GeneralizedLinearMixedModel.random
	random
	random
	LinearMixedModel.random
	random
	random
	RepeatedMeasuresModel.random
	GeneralizedLinearMixedModel.randomEffects
	LinearMixedModel.randomEffects
	randsample
	randtool
	range
	rangesearch
	rangesearch
	ranksum
	ranova
	raylcdf
	raylfit
	raylinv
	raylpdf
	raylrnd
	raylstat
	rcoplot
	ReconstructionICA
	Reduce Dimensionality
	refcurve
	GeneralizedLinearMixedModel.refit
	FeatureSelectionNCAClassification.refit
	FeatureSelectionNCARegression.refit
	reduceDimensions
	refline
	regress
	RegressionBaggedEnsemble
	RegressionEnsemble
	RegressionEnsemble Predict
	RegressionGAM
	RegressionGP
	RegressionGP Predict
	RegressionLinear
	RegressionLinear Predict
	RegressionLinearCoderConfigurer
	RegressionNeuralNetwork
	RegressionNeuralNetwork Predict
	RegressionPartitionedEnsemble
	RegressionPartitionedGAM
	RegressionPartitionedGP
	RegressionPartitionedLinear
	RegressionPartitionedModel
	RegressionPartitionedSVM
	RegressionSVM
	RegressionSVMCoderConfigurer
	RegressionSVM Predict
	RegressionTree
	RegressionTree Predict
	RegressionTreeCoderConfigurer
	regstats
	RegressionEnsemble.regularize
	relieff
	CompactClassificationEnsemble.removeLearners
	CompactRegressionEnsemble.removeLearners
	removeTerms
	removeTerms
	reorderlevels
	repartition
	RepeatedMeasuresModel
	dataset.replacedata
	dataset.replaceWithMissing
	report
	reset
	reset
	reset
	reset
	qrandstream.reset
	GeneralizedLinearMixedModel.residuals
	LinearMixedModel.residuals
	GeneralizedLinearMixedModel.response
	LinearMixedModel.response
	resubEdge
	ClassificationDiscriminant.resubEdge
	resubEdge
	ClassificationEnsemble.resubEdge
	ClassificationTree.resubEdge
	ClassificationDiscriminant.resubLoss
	resubLoss
	ClassificationEnsemble.resubLoss
	resubLoss
	ClassificationTree.resubLoss
	RegressionEnsemble.resubLoss
	resubLoss
	RegressionSVM.resubLoss
	RegressionTree.resubLoss
	resubMargin
	ClassificationDiscriminant.resubMargin
	resubMargin
	ClassificationEnsemble.resubMargin
	ClassificationTree.resubMargin
	ClassificationDiscriminant.resubPredict
	resubPredict
	ClassificationEnsemble.resubPredict
	resubPredict
	ClassificationTree.resubPredict
	RegressionEnsemble.resubPredict
	resubPredict
	RegressionSVM.resubPredict
	RegressionTree.resubPredict
	resume
	ClassificationEnsemble.resume
	ClassificationPartitionedEnsemble.resume
	resume
	resume
	RegressionEnsemble.resume
	RegressionPartitionedEnsemble.resume
	RegressionSVM.resume
	rica
	ridge
	robustcov
	robustdemo
	robustfit
	RobustRandomCutForest
	ROCCurve
	rocmetrics
	rotatefactors
	rowexch
	rrcforest
	rsmdemo
	rstool
	runstest
	sampsizepwr
	saveCompactModel
	saveLearnerForCoder
	scatterhist
	scramble
	segment
	ClassificationLinear.selectModels
	selectModels
	RegressionLinear.selectModels
	SemiSupervisedGraphModel
	SemiSupervisedSelfTrainingModel
	sequentialfs
	dataset.set
	CompactTreeBagger.setDefaultYfit
	dataset.setdiff
	setlabels
	dataset.setxor
	shapley
	RegressionEnsemble.shrink
	signrank
	signtest
	silhouette
	SilhouetteEvaluation
	dataset.single
	dataset.size
	slicesample
	skewness
	sobolset
	sortClasses
	dataset.sortrows
	sparsefilt
	SparseFiltering
	spectralcluster
	squareform
	dataset.stack
	qrandstream.State
	statget
	statset
	std
	step
	step
	stepwise
	stepwiseglm
	stepwiselm
	stepwisefit
	dataset.subsasgn
	dataset.subsref
	dataset.summary
	struct2dataset
	surfht
	survival
	table2dataset
	tabulate
	tblread
	tblwrite
	tcdf
	tdfread
	templateDiscriminant
	templateECOC
	templateEnsemble
	templateKernel
	templateKNN
	templateLinear
	templateNaiveBayes
	templateSVM
	templateTree
	test
	test
	testcholdout
	testckfold
	testDeviance
	tiedrank
	tinv
	tpdf
	training
	training
	transform
	transform
	transform
	TreeBagger
	trimmean
	trnd
	truncate
	tsne
	tspartition
	tstat
	ttest
	ttest2
	BetaDistribution
	BinomialDistribution
	BirnbaumSaundersDistribution
	BurrDistribution
	ExponentialDistribution
	ExtremeValueDistribution
	GammaDistribution
	GeneralizedExtremeValueDistribution
	GeneralizedParetoDistribution
	HalfNormalDistribution
	InverseGaussianDistribution
	KernelDistribution
	LogisticDistribution
	LoglogisticDistribution
	LognormalDistribution
	LoguniformDistribution
	MultinomialDistribution
	NakagamiDistribution
	NegativeBinomialDistribution
	NormalDistribution
	PiecewiseLinearDistribution
	PoissonDistribution
	RayleighDistribution
	RicianDistribution
	StableDistribution
	stats
	tLocationScaleDistribution
	TriangularDistribution
	UniformDistribution
	WeibullDistribution
	dataset.union
	dataset.unique
	unidcdf
	unidinv
	unidpdf
	unidrnd
	unidstat
	unifcdf
	unifinv
	unifit
	unifpdf
	unifrnd
	unifstat
	dataset.unstack
	update
	updateMetrics
	updateMetrics
	updateMetrics
	updateMetrics
	updateMetrics
	updateMetricsAndFit
	updateMetricsAndFit
	updateMetricsAndFit
	updateMetricsAndFit
	updateMetricsAndFit
	upperparams
	validatedUpdateInputs
	var
	varianceComponent
	vartest
	vartest2
	vartestn
	dataset.vertcat
	compact
	CompactClassificationTree.view
	CompactRegressionTree.view
	wblcdf
	wblfit
	wblinv
	wbllike
	wblpdf
	wblplot
	wblrnd
	wblstat
	wishrnd
	xptread
	x2fx
	zscore
	ztest
	hmcSampler
	HamiltonianSampler
	HamiltonianSampler.estimateMAP
	HamiltonianSampler.tuneSampler
	HamiltonianSampler.drawSamples
	HamiltonianSampler.diagnostics
	Experiment Manager
	Classification Learner
	Regression Learner
	Distribution Fitter
	fitckernel
	ClassificationKernel
	edge
	loss
	margin
	predict
	resume
	fitrkernel
	RegressionKernel
	loss
	predict
	resume
	RegressionPartitionedKernel
	kfoldLoss
	kfoldPredict
Sample Data Sets
	Sample Data Sets
Probability Distributions
	Bernoulli Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Examples
		Related Distributions
	Beta Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Examples
		Related Distributions
	Binomial Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Example
		Related Distributions
	Birnbaum-Saunders Distribution
		Definition
		Background
		Parameters
	Burr Type XII Distribution
		Definition
		Background
		Parameters
		Fit a Burr Distribution and Draw the cdf
		Compare Lognormal and Burr Distribution pdfs
		Burr pdf for Various Parameters
		Survival and Hazard Functions of Burr Distribution
		Divergence of Parameter Estimates
	Chi-Square Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Inverse Cumulative Distribution Function
		Descriptive Statistics
		Examples
		Related Distributions
	Exponential Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Inverse Cumulative Distribution Function
		Hazard Function
		Examples
		Related Distributions
	Extreme Value Distribution
		Definition
		Background
		Parameters
		Examples
	F Distribution
		Definition
		Background
		Examples
	Gamma Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Inverse Cumulative Distribution Function
		Descriptive Statistics
		Examples
		Related Distributions
	Generalized Extreme Value Distribution
		Definition
		Background
		Parameters
		Examples
	Generalized Pareto Distribution
		Definition
		Background
		Parameters
		Examples
	Geometric Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Hazard Function
		Examples
		Related Distributions
	Half-Normal Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Relationship to Other Distributions
	Hypergeometric Distribution
		Definition
		Background
		Examples
	Inverse Gaussian Distribution
		Definition
		Background
		Parameters
	Inverse Wishart Distribution
		Definition
		Background
		Example
	Kernel Distribution
		Overview
		Kernel Density Estimator
		Kernel Smoothing Function
		Bandwidth
	Logistic Distribution
		Overview
		Parameters
		Probability Density Function
		Relationship to Other Distributions
	Loglogistic Distribution
		Overview
		Parameters
		Probability Density Function
		Relationship to Other Distributions
	Lognormal Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Examples
		Related Distributions
	Loguniform Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Examples
		Related Distributions
	Multinomial Distribution
		Overview
		Parameter
		Probability Density Function
		Descriptive Statistics
		Relationship to Other Distributions
	Multivariate Normal Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Examples
	Multivariate t Distribution
		Definition
		Background
		Example
	Nakagami Distribution
		Definition
		Background
		Parameters
	Negative Binomial Distribution
		Definition
		Background
		Parameters
		Example
	Noncentral Chi-Square Distribution
		Definition
		Background
		Examples
	Noncentral F Distribution
		Definition
		Background
		Examples
	Noncentral t Distribution
		Definition
		Background
		Examples
	Normal Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Examples
		Related Distributions
	Piecewise Linear Distribution
		Overview
		Parameters
		Cumulative Distribution Function
		Relationship to Other Distributions
	Poisson Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Examples
		Related Distributions
	Rayleigh Distribution
		Definition
		Background
		Parameters
		Examples
	Rician Distribution
		Definition
		Background
		Parameters
	Stable Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Relationship to Other Distributions
	Student's t Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Inverse Cumulative Distribution Function
		Descriptive Statistics
		Examples
		Related Distributions
	t Location-Scale Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Relationship to Other Distributions
	Triangular Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Examples
	Uniform Distribution (Continuous)
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Descriptive Statistics
		Random Number Generation
		Examples
		Related Distributions
	Uniform Distribution (Discrete)
		Definition
		Background
		Examples
	Weibull Distribution
		Overview
		Parameters
		Probability Density Function
		Cumulative Distribution Function
		Inverse Cumulative Distribution Function
		Hazard Function
		Examples
		Related Distributions
	Wishart Distribution
		Overview
		Parameters
		Probability Density Function
		Example
Bibliography
	Bibliography