92 Applied Predictive Modeling Techniques in R

92 Applied Predictive Modeling Techniques in R: With step by step instructions on how to build them FAST!

About This Book

This jam-packed book takes you under the hood with step by step instructions using the popular and free R predictive analytics package. It provides numerous examples, illustrations and exclusive use of real data to help you leverage the power of predictive analytics. A book for every data analyst, student and applied researcher.

Here is what it can do for you:

• BOOST PRODUCTIVITY: Learn how to build predictive analytic models in less time than you ever imagined possible! Even if you’re a busy professional or a student with little time. By spending as little as 10 minutes a day working through the dozens of real world examples, illustrations, practitioner tips and notes, you’ll be able to make giant leaps forward in your knowledge, strengthen your business performance, broaden your skill-set and improve your understanding.
• SIMPLIFY ANALYSIS: You will discover over 90 easy to follow applied predictive analytic techniques that can instantly expand your modeling capability. Plus you’ll discover simple routines that serve as a check list you repeat next time you need a specific model. Even better, you’ll discover practitioner tips, work with real data and receive suggestions that will speed up your progress. So even if you’re completely stressed out by data, you’ll still find in this book tips, suggestions and helpful advice that will ease your journey through the data science maze.
• SAVE TIME: Imagine having at your fingertips easy access to the very best of predictive analytics. In this book, you’ll learn fast effective ways to build powerful models using R.
• LEARN FASTER:92 Applied Predictive Modeling Techniques in R offers a practical results orientated approach that will boost your productivity, expand your knowledge and create new and exciting opportunities for you to get the very best from your data.
• IMPROVE RESULTS : Want to improve your predictive analytic results, but don’t have enough time? Right now there are a dozen ways to instantly improve your predictive models performance. Odds are, these techniques will only take a few minutes apiece to complete. The problem? You might feel like there’s not enough time to learn how to do them all. The solution is in your hands. It uses R, which is free, open-source, and extremely powerful software.

Here is some of what is included:

• Support Vector Machines
• Relevance Vector Machines
• Neural networks
• Random forests
• Random ferns
• Classical Boosting
• Model based boosting
• Decision trees
• Cluster Analysis

For people interested in statistics, machine learning, data analysis, data mining, and future hands-on practitioners seeking a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. Buy the book today. Your next big breakthrough using predictive analytics is only a page away!

Table of Contents

Part I Decision Trees
Technique 1: Classification Tree
Technique 2: C5.0 Classification Tree
Technique 3: Conditional Inference Classification Tree
Technique 4: Evolutionary Classification Tree
Technique 5: Oblique Classification Tree
Technique 6: Logistic Model Based Recursive Partitioning
Technique 7: Probit Model Based Recursive Partitioning
Technique 8: Regression Tree
Technique 9: Conditional Inference Regression Tree
Technique 10: Linear Model Based Recursive Partitioning
Technique 11: Evolutionary Regression Tree
Technique 12: Poisson Decision Tree
Technique 13: Poisson Model Based Recursive Partitioning
Technique 14: Conditional Inference Ordinal Response Tree
Technique 15: Exponential Algorithm
Technique 16: Conditional Inference Survival Tree

Part II Support Vector Machines
Technique 17: Binary Response Classification with C-SVM
Technique 18: Multicategory Classification with C-SVM
Technique 19: Multicategory Classification with nu-SVM
Technique 20: Bound-constraint C-SVM classification
Technique 21: Weston – Watkins Multi-Class SVM
Technique 22: Crammer – Singer Multi-Class SVM
Technique 23: SVM eps-Regression
Technique 24: SVM nu-Regression
Technique 25: Bound-constraint SVM eps-Regression
Technique 26: One-Classification SVM

Part III Relevance Vector Machine
Technique 27: RVM Regression
Technique 28: Resilient Backpropagation with Backtracking
Technique 29: Resilient Backpropagation
Technique 30: Smallest Learning Rate
Technique 31: Probabilistic Neural Network
Technique 32: Multilayer Feedforward Neural Network
Technique 33: Resilient Backpropagation with Backtracking
Technique 34: Resilient Backpropagation
Technique 35: Smallest Learning Rate
Technique 36: General Regression Neural Network
Technique 37: Monotone Multi-Layer Perceptron
Technique 38: Quantile Regression Neural Network

Part V Random Forests
Technique 39: Classification Random Forest
Technique 40: Conditional Inference Classification Random Forest
Technique 41: Classification Random Ferns
Technique 42: Binary Response Random Forest
Technique 43: Binary Response Random Ferns
Technique 44: Survival Random Forest
Technique 45: Conditional Inference Survival Random Forest
Technique 46: Conditional Inference Regression Random Forest
Technique 47: Quantile Regression Forests
Technique 48: Conditional Inference Ordinal Random Forest

Part VI Cluster Analysis
Technique 49: K-Means
Technique 50: Clara Algorithm
Technique 51: PAM Algorithm
Technique 52: Kernel Weighted K-Means
Technique 53: Hierarchical Agglomerative Cluster Analysis
Technique 54: Agglomerative Nesting
Technique 55: Divisive Hierarchical Clustering
Technique 56: Exemplar Based Agglomerative Clustering
Technique 57: Rousseeuw-Kaufman’s Fuzzy Clustering Method
Technique 58: Fuzzy K-Means
Technique 59: Fuzzy K-Medoids
Technique 60: Density-Based Cluster Analysis
Technique 61: K-Modes Clustering
Technique 62: Model-Based Clustering
Technique 63: Clustering of Binary Variables
Technique 64: Affinity Propagation Clustering
Technique 65: Exemplar-Based Agglomerative Clustering
Technique 66: Bagged Clustering

Part VII Boosting
Technique 67: Ada Boost.M1
Technique 68: Real Ada Boost
Technique 69: Gentle Ada Boost
Technique 70: Discrete L2 Boost
Technique 71: Real L2 Boost
Technique 72: Gentle L2 Boost
Technique 73: SAMME
Technique 74: Breiman’s Extension
Technique 75: Freund’s Adjustment
Technique 76: L2 Regression
Technique 77: L1 Regression
Technique 78: Robust Regression
Technique 79: Generalized Additive Model
Technique 80: Quantile Regression
Technique 81: Expectile Regression
Technique 82: Logistic Regression
Technique 83: Probit Regression
Technique 84: Poisson Regression
Technique 85: Negative Binomial Regression
Technique 86: Hurdle Regression
Technique 87: Proportional Odds Model
Technique 88: Weibull Accelerated Failure Time Model
Technique 89: Lognormal Accelerated Failure Time Model
Technique 90: Log-logistic Accelerated Failure Time Model
Technique 91: Cox Proportional Hazard Model
Technique 92: Gehan Loss Accelerated Failure Time Model