Statistics and Machine Learning Toolbox. User's Guide. R2023b

• Publisher: MathWorks
• Year: 2023
• Tongue: English
• Leaves: 11716
• Category: Library

✦ Table of Contents

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
Perform Multivariate Analysis of Variance (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
Multinomial Models for Hierarchical Responses
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
Manually Perform Time Series Forecasting Using Ensembles of Boosted Regression Trees
Perform Time Series Direct Forecasting with directforecaster
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
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
Explain Model Predictions for Classifiers Trained in Classification Learner App
Explain Local Model Predictions Using LIME Values
Explain Local Model Predictions Using Shapley Values
Interpret Model Using Partial Dependence Plots
Use Partial Dependence Plots to 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
Efficiently Trained Linear Regression Models
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
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
Compare Linear Regression Models 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
Explain Model Predictions for Regression Models Trained in Regression Learner App
Explain Local Model Predictions Using LIME Values
Explain Local Model Predictions Using Shapley Values
Interpret Model Using Partial Dependence Plots
Use Partial Dependence Plots to 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
Incremental Anomaly Detection Overview
What Is Incremental Anomaly Detection?
Incremental Anomaly Detection with MATLAB
Configure Incremental Learning Model
Call Object Directly
Convert Traditionally Trained Model
Configure Model for Incremental Anomaly Detection
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
Monitor Equipment State of Health Using Drift-Aware Learning on the Cloud
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
Predict Class Labels Using ClassificationNaiveBayes 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
Find Nearest Neighbors Using KNN Search Block
Perform Incremental Learning Using IncrementalRegressionLinear Fit and Predict Blocks
Perform Incremental Learning Using IncrementalClassificationLinear Fit and Predict Blocks
Perform Incremental Learning and Track Performance Metrics Using Update Metrics Block
Functions
addedvarplot
addK
addlevels
addInteractions
qrandstream.addlistener
addMetrics
GeneralizedLinearMixedModel.anova
addTerms
addTerms
adtest
andrewsplot
anova
anova
anova
anova1
anova2
anovan
anova
ansaribradley
aoctool
TreeBagger.append
average
barttest
barttest
BayesianOptimization
bayesopt
bbdesign
bestPoint
betacdf
betafit
betainv
betalike
betapdf
betarnd
betastat
binocdf
binofit
binoinv
binopdf
binornd
binostat
binScatterPlot
biplot
bootci
bootstrp
boxchart
boxchart
boxplot
boundary
CalinskiHarabaszEvaluation
candexch
candgen
canoncorr
canonvars
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
KNN Search
ClassificationKNN Predict
ClassificationLinear
ClassificationLinear Predict
ClassificationLinearCoderConfigurer
ClassificationNaiveBayes
ClassificationNaiveBayes Predict
ClassificationNeuralNetwork
ClassificationNeuralNetwork Predict
IncrementalClassificationLinear Predict
IncrementalClassificationLinear Fit
IncrementalRegressionLinear Predict
IncrementalRegressionLinear Fit
Update Metrics
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
coefCI
coefCI
coefCI
coefTest
GeneralizedLinearMixedModel.coefTest
coefTest
coefTest
coeftest
coefTest
coefTest
coeftest
CompactTreeBagger.combine
combnk
compact
compact
compact
ClassificationEnsemble.compact
compact
compact
compact
compact
RegressionEnsemble.compact
RegressionSVM.compact
compact
TreeBagger.compact
CompactClassificationDiscriminant
CompactClassificationECOC
CompactClassificationEnsemble
ClassificationGAM
CompactClassificationNaiveBayes
CompactClassificationNeuralNetwork
CompactClassificationGAM
CompactClassificationSVM
CompactClassificationTree
CompactDirectForecaster
CompactLinearModel
CompactGeneralizedLinearModel
CompactRegressionEnsemble
CompactRegressionGAM
CompactRegressionGP
CompactRegressionNeuralNetwork
CompactRegressionSVM
CompactRegressionTree
CompactTreeBagger
GeneralizedLinearMixedModel.compare
compare
compareHoldout
confusionchart
ConfusionMatrixChart
confusionmat
controlchart
controlrules
cophenet
copulacdf
copulafit
copulaparam
copulapdf
copulastat
copularnd
cordexch
corr
corrcov
GeneralizedLinearMixedModel.covarianceParameters
covarianceParameters
CoxModel
coxphfit
createns
crosstab
crossval
crossval
crossval
crossval
ClassificationEnsemble.crossval
crossval
crossval
RegressionEnsemble.crossval
RegressionSVM.crossval
crossval
cvloss
cvloss
cvloss
cvpartition
cvpredict
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
designMatrix
directforecaster
discardResiduals
discardSupportVectors
discardSupportVectors
CompactRegressionSVM.discardSupportVectors
dataset.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
edge
edge
CompactClassificationEnsemble.edge
edge
edge
edge
edge
dataset.end
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
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
fitted
GeneralizedLinearMixedModel.fixedEffects
fixedEffects
forecast
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
groupmeans
TreeBagger.growTrees
grp2idx
grpstats
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
incrementalOneClassSVM
incrementalRobustRandomCutForest
incrementalDriftAwareLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalLearner
incrementalRegressionKernel
incrementalRegressionLinear
dataset.isempty
isanomaly
isanomaly
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
logp
logp
logp
loss
loss
ClassificationLinear.loss
loss
loss
CompactClassificationEnsemble.loss
loss
loss
loss
loss
CompactRegressionEnsemble.loss
CompactRegressionGP.loss
loss
CompactRegressionSVM.loss
loss
loss
loss
loss
loss
loss
loss
loss
FeatureSelectionNCAClassification.loss
FeatureSelectionNCARegression.loss
loss
RegressionLinear.loss
lowerparams
qrandstream.lt
lsline
mad
mahal
mahal
mahal
maineffectsplot
makecdiscr
makedist
manova
manova
manova1
manovacluster
margin
margin
ClassificationLinear.margin
margin
margin
CompactClassificationEnsemble.margin
margin
margin
margin
margin
CompactTreeBagger.margin
TreeBagger.margin
margmean
mauchly
mat2dataset
mdscale
CompactTreeBagger.mdsprox
TreeBagger.mdsprox
mean
meanEffectSize
CompactTreeBagger.meanMargin
TreeBagger.meanMargin
surrogateAssociation
surrogateAssociation
median
mergelevels
mhsample
mle
mlecov
mnpdf
mnrfit
mnrnd
mnrval
moment
multcompare
MultinomialRegression
multcompare
multcompare
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
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
PartitionedDirectForecaster
pca
pcacov
perObservationLoss
perObservationLoss
perObservationLoss
pcares
ppca
pdf
pdf
pdist
pdist2
pearscdf
pearspdf
pearsrnd
perfcurve
plot
plot
plot
plot
plot
plot
plot
plot
plotAdded
plotAdjustedResponse
plot
plotComparisons
plotDiagnostics
plotDiagnostics
plotDiagnostics
plotEffects
plotInteraction
plotLocalEffects
plotPartialDependence
plotprofile
plotprofile
plotResiduals
GeneralizedLinearMixedModel.plotResiduals
plotResiduals
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
predict
predict
CompactClassificationEnsemble.predict
predict
predict
predict
predict
CompactRegressionEnsemble.predict
CompactRegressionGP.predict
predict
CompactRegressionSVM.predict
predict
predict
predict
predict
predict
predict
predict
predict
predict
RegressionLinear.predict
CompactTreeBagger.predict
predict
GeneralizedLinearMixedModel.predict
predict
predict
predict
FeatureSelectionNCAClassification.predict
FeatureSelectionNCARegression.predict
predict
predict
predict
predict
TreeBagger.predict
predictConstraints
predictError
predictObjective
predictObjectiveEvaluationTime
CompactClassificationEnsemble.predictorImportance
predictorImportance
CompactRegressionEnsemble.predictorImportance
predictorImportance
preparedPredictors
probplot
procrustes
proflik
CompactTreeBagger.proximity
prune
prune
qrandstream.qrand
qrandstream
qrandstream
qqplot
qrandstream.rand
TreeBagger.quantileError
TreeBagger.quantilePredict
randg
random
random
GeneralizedLinearMixedModel.random
random
random
random
random
random
random
GeneralizedLinearMixedModel.randomEffects
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
RegressionPartitionedNeuralNetwork
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
reset
reset
qrandstream.reset
GeneralizedLinearMixedModel.residuals
residuals
GeneralizedLinearMixedModel.response
response
resubEdge
resubEdge
resubEdge
ClassificationEnsemble.resubEdge
resubEdge
resubLoss
resubLoss
ClassificationEnsemble.resubLoss
resubLoss
resubLoss
RegressionEnsemble.resubLoss
resubLoss
RegressionSVM.resubLoss
resubLoss
resubMargin
resubMargin
resubMargin
ClassificationEnsemble.resubMargin
resubMargin
resubPredict
resubPredict
ClassificationEnsemble.resubPredict
resubPredict
resubPredict
RegressionEnsemble.resubPredict
resubPredict
RegressionSVM.resubPredict
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
selectFeatures
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
templateGAM
templateGP
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
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
view
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
Pearson Distribution
Types
Parameters
Probability Density Function
Cumulative Distribution Function
Support
Examples
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