Overview

MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. This book develops the work with hypothesys tests, analysis of variance and bayesian optimization. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. Statistics and Machine Learning Toolbox provides parametric and nonparametric hypothesis tests to help you determine if your sample data comes from a population with particular characteristics. Distribution tests, such as Anderson-Darling and one-sample Kolmogorov-Smirnov, test whether sample data comes from a population with a particular distribution. Test whether two sets of sample data have the same distribution using tests such as two-sample Kolmogorov-Smirnov. Location tests, such as z-test and one-sample t-test, test whether sample data comes from a population with a particular mean or median. Test two or more sets of sample data for the same location value using a two-sample t-test or multiple comparison test. Dispersion tests, such as Chi-square variance, test whether sample data comes from a population with a particular variance. Compare the variances of two or more sample data sets using a two-sample F-test or multiple-sample test. Determine additional features of sample data by cross-tabulating, conducting a run test for randomness, and determine the sample size and power for a hypothesis test. Analysis of Variance (ANOVA) is a procedure for determining whether variation in the response variable arises within or among different population groups. Statistics and Machine Learning Toolbox provides one-way, two-way, and N-way analysis of variance (ANOVA); multivariate analysis of variance (MANOVA); repeated measures models; and analysis of covariance (ANCOVA). Optimization, in its most general form, is the process of locating a point that minimizes a real-valued function called the objective function. Bayesian optimization is the name of one such process. Bayesian optimization internally maintains a Gaussian process model of the objective function, and uses objective function evaluations to train the model. One innovation in Bayesian optimization is the use of an acquisition function, which the algorithm uses to determine the next point to evaluate. The acquisition function can balance sampling at points that have low modeled objective functions, and exploring areas that have not yet been modeled well. For details, see Bayesian Optimization Algorithm. Bayesian optimization is part of Statistics and Machine Learning Toolbox because it is well-suited to optimizing hyperparameters of classification and regression algorithms. A hyperparameter is an internal parameter of a classifier or regression function, such as the box constraint of a support vector machine, or the learning rate of a robust classification ensemble. These parameters can strongly affect the performance of a classifier or regressor, and yet it is typically difficult or time-consuming to optimize them. See Bayesian Optimization Characteristics. Typically, optimizing the hyperparameters means you try to minimize the cross-validation loss of a classifier or regression.

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