Predictive Analytics using R
Lulu.com, 16 gen 2015 - 552 pagine
This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling. A predictive model is a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics - it is an advanced book. Some theoretical foundations are laid out but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. You may download R, from a preferred CRAN mirror at http: //www.r-project.org/. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this a textbook with problem solving in R-but there are no "by-hand" exercises.
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Empirical Bayes method
Naïve Bayes classifier
Decision tree learning
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𝐴 adaptive regression splines ANOVA applications artificial neural network assumption autoregressive 𝐵 basis functions Bayesian classification clustering computing data mining data set decision trees dendrogram dependent variable deviance error estimate example explanatory variables formula gradient boosting hinge functions hyperplane 𝑖 input interaction 𝑘 𝑘-NN leaf learning algorithms least squares likelihood linear model linear regression logistic regression machine learning MARS models matrix mean measure members at h method multinomial Multivariate Adaptive Regression 𝑛 node normal distribution null observations optimization outcome outliers output 𝑝 parameters plot predictive analytics predictive modeling predictor probit problem random forest regression analysis regression coefficients regression model regression trees regressors residuals response variable robust sample saturated model simulators statistical model sum of squares supervised learning support vector machines 𝑡 techniques training data training set treatment Uplift modeling values variance weights 𝑥 𝑦