Data Analytics Across Multivariate Statistics Methods Using Matlab PDF Download

Large, high-dimensional data sets are common in the modern era of computer-based instrumentation and electronic data storage. High-dimensional data present many challenges for statistical visualization, analysis, and modeling.Data visualization, of course, is impossible beyond a few dimensions. As a result, pattern recognition, data preprocessing, and model selection must rely heavily on numerical methods. The most important contents of this book are: Multivariate Linear Regression Estimation of Multivariate Regression Models Multivariate General Linear Model Fixed Effects Panel Model with Concurrent Longitudinal Analysis Multidimensional Scaling Procrustes Analysis Feature Selection Feature Transformation Principal Component Analysis (PCA) Factor Analysis Partial Least Squares Regression and Principal Components Regression Cluster Analysis Hierarchical Clustering Algorithm Description Dendrograms k-Means Clustering Gaussian Mixture Models Cluster with Gaussian Mixtures Parametric Classification Discriminant Analysis What Is Discriminant Analysis? Naive Bayes Classification Supported Distributions Performance Curves Nonparametric Supervised Learning Supervised Learning (Machine Learning) Workflow and Algorithms Steps in Supervised Learning (Machine Learning) Characteristics of Algorithms Classification Using Nearest Neighbors Pairwise Distance k-Nearest Neighbor Search and Radius Search K-Nearest Neighbor Classification for Supervised Learning Construct a KNN Classifier Examine the Quality of a KNN Classifier Predict Classification Based on a KNN Classifier Modify a KNN Classifier Classification Trees and Regression Trees What Are Classification Trees and Regression Trees? Creating a Classification Tree Creating a Regression Tree Viewing a Tree How the Fit Methods Create Trees Predicting Responses With Classification and Regression Trees Improving Classification Trees and Regression Trees Splitting Categorical Predictors Challenges in Splitting Multilevel Predictors Pull Left By Purity Principle Component-Based Partitioning One Versus All By Class Ensemble Methods Framework for Ensemble Learning Basic Ensemble Examples Test Ensemble Quality Classification with Imbalanced Data Classification: Imbalanced Data or Unequal Misclassification Costs Classification with Many Categorical Levels Surrogate Splits LPBoost and TotalBoost for Small Ensembles Ensemble Regularization Tuning RobustBoost Random Subspace Classification TreeBagger Examples Ensemble Algorithms Support Vector Machines (SVM) Understanding Support Vector Machines Using Support Vector Machines Nonlinear Classifier with Gaussian Kernel SVM Classification with Cross Validation