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Foundations of predictive analytics
\"Preface this text is a summary of techniques of data analysis and modeling that the authors have encountered and used in our two-decades experience of practicing the art of applied data mining across many different fields. The authors have worked in this field together and separately in many large and small companies, including the Los Alamos National Laboratory, Bank One (JPMorgan Chase), Morgan Stanley, and the startups of the Center for Adaptive Systems Applications (CASA), the Los Alamos Computational Group and ID Analytics. We have applied these techniques to traditional and nontraditional problems in a wide range of areas including consumer behavior modeling (credit, fraud, marketing), consumer products, stock forecasting, fund analysis, asset allocation, and equity and xed income options pricing. This monograph provides the necessary information for understanding the common techniques for exploratory data analysis and modeling. It also explains the details of the algorithms behind these techniques, including underlying assumptions and mathematical formulations. It is the authors' opinion that in order to apply di erent techniques to di erent problems appropriately, it is essential to understand the assumptions and theory behind each technique. It is recognized that this work is far from a complete treatise on the subject. Many excellent additional texts exist on the popular subjects and it was not a goal for this present text to be a complete compilation. Rather this text contains various discussions on many practical subjects that are frequently missing from other texts, as well as details on some subjects that are not often or easily found. Thus this text makes an excellent supplemental and referential resource for the practitioners of these subjects\"--Provided by publisher.
Book
Foundations of Predictive Analytics
Drawing on the authors' two decades of experience in applied modeling and data mining, this self-contained book presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It explains the algorithmic details behind each technique, including underlying assumptions and mathematical formulations, and discusses a variety of practical topics that are frequently missing from similar texts. Software and examples are available at www.DataMinerXL.com.
eBook
Model Goodness Measures
In the construction of models one attempts to find the best fit of a mathematical expression (formula, rules, ...) to a set of given data by adjusting
free parameters in the model. This can be thought of as fitting an (n − 1)–
dimensional hypersurface to points existing in an n-dimensional space. In this
fitting process one tries to find this set of best parameters according to some
definition of “best.” Included in this concept of best are (1) some quantitative
measure of goodness of fit to an objective function and (2) the need for the
model to generalize beyond the particular given data set. Generally these are
competing and somewhat conflicting goals. Specifically, one can fit the model
exactly to the given data, but when new data comes sometimes the fit is not
as good for this new data. Thus the standard practice of separating data into
training, testing, and validation sets has become routine in machine learning.
In this chapter we discuss a variety of topics around the concept of model
fitting/generalization and the ways to measure the goodness of a model fit. If
a model fits the training data well but not the testing data we say that the
model is overfit, which is a cardinal sin in modeling.
Book Chapter
Optimization Methods
The search for optimality can be thought of as a search through the space
formed by the union of all free parameters and some objective function. This
can be easily envisioned in three dimensions, and easily extended conceptually to higher dimensions. Consider a situation where we have only two free
parameters and an objective function, which of course will vary depending
on the value of the free parameters. Typically, we are trying to fit a model
by adjusting these free parameters in such a way as to optimize an objective
function: minimize an error or maximize some other modeled performance
(profit, revenue). The model designer has complete freedom in selecting both
the model structure as well as formulating an appropriate objective function
that best suits the goals of the problem.
Book Chapter
Properties of Statistical Distributions
This chapter presents common distributions widely used in data analysis and
modeling. Fully understanding these distributions is key to understanding the
underlying assumptions about the distributions of data. The next chapter
will discuss various aspects of matrix theory, which is a convenient vehicle to
formulate many of these types of problems.
Book Chapter
Time Series Analysis
Modeling for time series is conceptually similar to other modeling problems, but one major distinction is that usually the next value of the series is
highly related to the most recent values, with a time-decaying importance in
this relationship to previous values. Because of this property a different set of
machinery has evolved for the special case of time series modeling, sometimes
loosely called forecasting or even prediction.
Book Chapter
Nonlinear Modeling
In Chapter 4, we discussed many of the important and popular linear modeling techniques. This chapter is devoted to the subject of nonlinear modeling
techniques. We will cover neural networks, decision trees, additive models,
support vector machine (SVM), and fuzzy logic systems.
Book Chapter