DECISION TREES, DISCRIMINANT ANALYSIS, LOGISTIC REGRESSION, SVM, ENSAMBLE METHODS and KNN with MATLAB

This book develops Advenced Predicive Tecniques: Decision Trees, Discriminant Analysis, Classification Learner (decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification) and Regression Learner (linear regression models, regression trees, Gaussian process regression models, support vector machines, and ensembles of regression tres).Decision trees, or classification trees and regression trees, predict responses to...
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This book develops Advenced Predicive Tecniques: Decision Trees, Discriminant Analysis, Classification Learner (decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification) and Regression Learner (linear regression models, regression trees, Gaussian process regression models, support vector machines, and ensembles of regression tres).Decision trees, or classification trees and regression trees, predict responses to data. To predict a response, follow the decisions in the tree from the root (beginning) node down to a leaf node. The leaf node contains the response. Classification trees give responses that are nominal, such as 'true' or 'false'. Regression trees give numeric responses. Statistics and Machine Learning Toolbox trees are binary. Each step in a prediction involves checking the value of one predictor (variable).Discriminant analysis is a classification method. It assumes that different clases generate data based on different Gaussian distributions. To train (create) a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class. To predict the classes of new data, the trained classifier finds the class with the smallest misclassification cost . Linear discriminant analysis is also known as the Fisher discriminant, named for its inventor.Use the Classification Learner app to train models to classify data using supervisedmachine learning. The app lets you explore supervised machine learning interactively using various classifiers. Automatically train a selection of models and help you choose the best model. Modeltypes include decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification.You can use Regression Learner to 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 process regression models, support vector machines, and ensembles of regression trees.Support vector machine (SVM) analysis is a popular machine learning tool forclassification and regression, first identified by Vladimir Vapnik and his colleagues. SVM regression is considered a nonparametric technique because it relies on kernel functions.