Publisher's Synopsis
MATLAB Statistics and Machine Learning Toolbox allows you work with data science techniques . It's posible to fit predicive models and work with classification techniques. This book develops the Predictive techniques in the Data Science: Multidimensional Linear Regression Model, Learner techniques (linear regression models, regression trees, Gaussian process regression models, Support Vector Machines, and ensembles of regression trees), Neural Networks Regression, Generalized Linear Models (GLM), Nonlinear Regression, Decision Trees, Discriminant Analysis and Naive Bayes The most important content is the following: -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 -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 and Validation for Regression Problem -Linear Regression Models -Regression Trees -Support Vector Machines -Gaussian Process Regression Models -Ensembles of Trees -Feature Selection -Feature Transformation -Assess Model Performance -Check Performance in History List -Evaluate Model Using Residuals Plot -Export Regression Model to Predict New Data -Train Regression Trees Using Regression Learner App -Mathematical Formulation of SVM Regression -Solving the SVM Regression Optimization Problem -Fit Regression Models with a Neural Network -Multinomial Models for Nominal Responses -Multinomial Models for Ordinal Responses -Hierarchical Multinomial Models -Generalized Linear Models -Lasso Regularization of Generalized Linear Models -Regularize Poisson Regression -Regularize Logistic Regression -Regularize Wide Data in Parallel -Generalized Linear Mixed-Effects Models -Fit a Generalized Linear Mixed-Effects Model -Regression with Neural Networks -Nonlinear Regression -Fit Nonlinear Model to Data -Examine Quality and Adjust the Fitted Nonlinear Model -Predict or Simulate Responses Using a Nonlinear Model -Mixed-Effects Models -Decision Trees -Discriminanat Analysis -Naive Bayes