Data Science with Matlab. Predictive Techniques: Multivariate Linear Regression and Regression Learner
Author | : A. Vidales |
Publisher | : Independently Published |
Total Pages | : 268 |
Release | : 2019-02-10 |
Genre | : Business & Economics |
ISBN | : 9781796598124 |
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Data science includes a set of statistical techniques that allow extracting the knowledge immersed in the data automatically. One of the fundamental techniques in data science is the treatment of regression models. Regression is the process of fitting models to data. The models must have numerical responses. The regression process depends on the model. If a model is parametric, regression estimates the parameters from the data. If a model is linear in the parameters, estimation is based on methods from linear algebra that minimize the norm of a residual vector. If a model is nonlinear in the parameters, estimation is based on search methods from optimization that minimize the norm of a residual vector.The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models.MATLAB provides tools to help you try out a variety of machine learning models and choose the best. Multivariate statistical techniques include supervised and unsupervised learning techniques. This book develops supervised analysis techniques such as multivariate regression models and data panel models. It also develops techniques such as dimension reduction, feature selection, feature transformation and multidimensional scaling. These techniques are usually grouped in MATLAB in the tool Regression Learner