راهنمای کاربران جعبه‌ابزار آمار و یادگیری ماشین MATLAB (نسخهٔ R2024b)

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 Visualize 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 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 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 Predict Responses Using Custom Python Model in Simulink 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 Predict Cluster Assignments Using Python Scikit-learn Model Predict Block 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 Identify Noisy Labels Using Confident Learning 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 View Model Information and Results in Compare Results Plot Plot Classifier Results Check Performance Per Class in the Confusion Matrix Check ROC Curve Check Precision-Recall 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 Export Classification Model to Make Predictions in Simulink 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 Global and Local Interpretation Plots Explain Local Model Predictions Using LIME Values Explain Local Model Predictions Using Shapley Values Explain Global Model Predictions Using Shapley Importance Plot Explain Global Model Predictions Using Shapley Summary Plot Explain Global Model Predictions Using Shapley Dependence Plot Adjust Global Shapley Plot Parameters 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 View Model Information and Results in Compare Results Plot 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 Export Regression Model to Make Predictions in Simulink 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 Global and Local Interpretation Plots Explain Local Model Predictions Using LIME Values Explain Local Model Predictions Using Shapley Values Explain Global Model Predictions Using Shapley Importance Plot Explain Global Model Predictions Using Shapley Summary Plot Explain Global Model Predictions Using Shapley Dependence Plot Adjust Global Shapley Plot Parameters 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 Partitio