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In this book we will develop Machine Learning techniques related to non-linear regression. More specifically, we will go deeper into non-linear multiple regression models with all their identification, estimation and diagnosis problems. Special emphasis is placed on generalised linear models and all types of derived non-linear models: Logit Models, Probit Models, Poisson Models and Negative Binomial Models. This is followed by models of limited dependent variable, discrete choice, count, censored, truncated and sample selection. Non-linear models with panel data are also discussed in depth. An important section is devoted to predictive models of neuroanalytic networks. All chapters are illustrated with examples and representative exercises solved with the latest software such as R, SAS, SPSS, EVIEWS and Statgraphics.
In the field of Machine Learning, there are different types of models that are used to analyse and predict data. Two of the most common types are linear models and non-linear models. Machine Learning uses two types of techniques: supervised learning, which trains models on known input and output data in order to predict future results, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data.
The goal of supervised machine learning is to build a model that makes evidence-based predictions 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