Predictive analytics - Unabridged Guide

Here's part of the content - you would like to know it all? Delve into this book today!..... : For a health insurance provider, predictive analytics can analyze a few years of past medical claims data, as well as lab, pharmacy and other records where available, to predict how expensive an enrollee is likely to be in the future.

...While mathematically it is feasible to apply multivariate regression to discrete ordered dependent variables, some of the assumptions behind the theory of multivariate linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis.

... In a classification setting, assigning outcome probabilities to observations can be achieved through the use of a logistic model, which is basically a method which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model (See Allison's Logistic Regression for more information on the theory of Logistic Regression).

...It can be proved that, unlike other methods, this method is universally asymptotically convergent, i. e. : as the size of the training set increases, if the observations are independent and identically distributed (i. i. d. ), regardless of the distribution from which the sample is drawn, the predicted class will converge to the class assignment that minimizes misclassification error.

There is absolutely nothing that isn't thoroughly covered in the book. It is straightforward, and does an excellent job of explaining all about Predictive analytics in key topics and material. There is no reason to invest in any other materials to learn about Predictive analytics. You'll understand it all.

Inside the Guide: Predictive analytics, Zementis Inc, Underwriting, Tibco Software, SPSS, Roger Jones (physicist and entrepreneur), RiskAoA, Pruning (decision trees), Predictive Model Markup Language, Prediction, Physics, Pattern recognition, Neural network, Naive Bayes classifier, Multivariate adaptive regression splines, Multinomial logit, Marketing, Machine learning, Linear regression, Learning analytics, k-nearest neighbor algorithm, In-database processing, Game theory, Fraud, Forecasting, Feed forward (control), Decision tree learning, Data mining, Customer attrition, Curse of dimensionality, Cross-selling, Criminal Reduction Utilising Statistical History, Credit card fraud, Control theory, Autoregressive model, Autoregressive integrated moving average