Mastering predictive analytics with R : master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts / Rui Miguel Forte.

Format:

Book

Author/Creator:

Forte, Rui Miguel, author.

Series:

Community experience distilled.

Community Experience Distilled

Language:

English

Subjects (All):

Neural networks (Computer science).

R (Computer program language).

Physical Description:

1 online resource (414 p.)

Edition:

1st edition

Other Title:

Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt Publishing, 2015.

Language Note:

English

System Details:

text file

Summary:

This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and c

Contents:

""Cover ""; ""Copyright""; ""Credits""; ""About the Author""; ""Acknowledgments""; ""About the Reviewers""; ""www.PacktPub.com""; ""Preface""; ""Chapter 1: Gearing Up for Predictive Modeling ""; ""Models""; ""Learning from data""; ""The core components of a model""; ""Our first model: k-nearest neighbors""; ""Types of models""; ""Supervised, unsupervised, semi-supervised, and reinforcement learning models""; ""Parametric and nonparametric models""; ""Regression and classification models""; ""Real time and batch machine learning models""; ""The process of predictive modeling""

""Defining the model's objective""""Collecting the data""; ""Picking a model""; ""Pre-processing the data""; ""Exploratory data analysis""; ""Feature transformations""; ""Encoding categorical features""; ""Missing data""; ""Outliers""; ""Removing problematic features""; ""Feature engineering and dimensionality reduction""; ""Training and assessing the model""; ""Repeating with different models and final model selection""; ""Deploying the model""; ""Performance metrics""; ""Assessing regression models""; ""Assessing classification models""; ""Assessing binary classification models""

""Summary""""Chapter 2 : Linear Regression""; ""Linear regression""; ""Assumptions of linear regression""; ""Simple linear regression""; ""Estimating the regression coefficients""; ""Multiple linear regression""; ""Predicting CPU performance""; ""Predicting the price of used cars""; ""Assessing linear regression models""; ""Residual analysis""; ""Significance tests for linear regression""; ""Performance metrics for linear regression""; ""Comparing different regression models""; ""Test set performance""; ""Problems with linear regression""; ""Multicollinearity""; ""Outliers""

""Feature selection""""Regularization""; ""Ridge regression""; ""Least absolute shrinkage and selection operator (lasso)""; ""Implementing regularization in R""; ""Summary""; ""Chapter 3 : Logistic Regression""; ""Classifying with linear regression""; ""Logistic regression""; ""Generalized linear models""; ""Interpreting coefficients in logistic regression""; ""Assumptions of logistic regression""; ""Maximum likelihood estimation""; ""Predicting heart disease""; ""Assessing logistic regression models""; ""Model deviance""; ""Test set performance""; ""Regularization with the lasso""

""Classification metrics""""Extensions of the binary logistic classifier""; ""Multinomial logistic regression""; ""Predicting glass type""; ""Ordinal logistic regression""; ""Predicting wine quality""; ""Summary""; ""Chapter 4 : Neural Networks""; ""The biological neuron""; ""The artificial neuron""; ""Stochastic gradient descent""; ""Gradient descent and local minima""; ""The perceptron algorithm""; ""Linear separation""; ""The logistic neuron""; ""Multilayer perceptron networks""; ""Training multilayer perceptron networks""; ""Predicting the energy efficiency of buildings""

""Evaluating multilayer perceptrons for regression""

Notes:

Includes index.

Description based on online resource; title from PDF title page (ebrary, viewed July 2, 2015).

OCLC:

913922370