MULTIVARIATE DATA ANALYSIS with MATLAB. KNN, ENSAMBLE, BOOSTING, BAGGING, RANDOM FOREST and SVM

This book develops Advanced Multivariate Methods for Prediction and Clasification: Nearest Neighbors, KNN Classifier, Ensemble Learning, Classification Ensemble, Regression Ensemble, Boosting, Bagging, Bagging of Regression Trees, Bagging of Classification Trees, Quantile Regression, Random Forest, Support Vector Machines for Binary Classification, Clasification Leaner Techniques and Regression Learner Techniques. This techniques are very important for work in Data Science. In...
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This book develops Advanced Multivariate Methods for Prediction and Clasification: Nearest Neighbors, KNN Classifier, Ensemble Learning, Classification Ensemble, Regression Ensemble, Boosting, Bagging, Bagging of Regression Trees, Bagging of Classification Trees, Quantile Regression, Random Forest, Support Vector Machines for Binary Classification, Clasification Leaner Techniques and Regression Learner Techniques. This techniques are very important for work in Data Science. In addition, the book also develops examples and applications relating to such methods.Classification Learner Automatically train a selection of models and help you choose the best model. Model types include decision trees, discriminant analysis, support vector machines, logisticregression, nearest neighbors, and ensemble classification.Regression Learner train regression models to predict data. Using thisapp, you can explore your data, select features, specify validation schemes, train models,and assess results. You can perform automated training to search for the best regressionmodel type, including linear regression models, regression trees, Gaussian processregression models, support vector machines, and ensembles of regression trees.