book

Introduction to Machine Learning with R

by Scott V. Burger

March 2018

Beginner to intermediate

223 pages

5h 38m

English

O'Reilly Media, Inc.

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Who Should Read This Book?Scope of the BookConventions Used in This BookO’Reilly SafariHow to Contact UsAcknowledgments
Algorithms Versus Models: What’s the Difference?A Note on TerminologyModeling LimitationsStatistics and Computation in ModelingData TrainingCross-ValidationWhy Use R?The GoodR and Machine LearningThe BadSummary
Supervised ModelsRegressionTraining and Testing of DataClassificationLogistic RegressionSupervised Clustering MethodsMixed MethodsTree-Based ModelsRandom ForestsNeural NetworksSupport Vector MachinesUnsupervised LearningUnsupervised Clustering MethodsSummary
BiasSampling in RTraining and TestingRoles of Training and Test SetsWhy Make a Test Set?Training and Test Sets: Regression ModelingTraining and Test Sets: Classification ModelingCross-Validationk-Fold Cross-ValidationSummary
Linear RegressionMultivariate RegressionRegularizationPolynomial RegressionGoodness of Fit with Data—The Perils of OverfittingRoot-Mean-Square ErrorModel Simplicity and Goodness of FitLogistic RegressionThe Motivation for ClassificationThe Decision BoundaryThe Sigmoid FunctionBinary ClassificationMulticlass ClassificationLogistic Regression with CaretSummaryLinear RegressionLogistic Regression
Single-Layer Neural NetworksBuilding a Simple Neural Network by Using RMultiple Compute OutputsHidden Compute NodesMultilayer Neural NetworksNeural Networks for RegressionNeural Networks for ClassificationNeural Networks with caretRegressionClassificationSummary
A Simple Tree ModelDeciding How to Split TreesTree Entropy and Information GainPros and Cons of Decision TreesTree OverfittingPruning TreesDecision Trees for RegressionDecision Trees for ClassificationConditional Inference TreesConditional Inference Tree RegressionConditional Inference Tree ClassificationRandom ForestsRandom Forest RegressionRandom Forest ClassificationSummary
Naive Bayes ClassificationBayesian Statistics in a NutshellApplication of Naive BayesPrincipal Component AnalysisLinear Discriminant AnalysisSupport Vector Machinesk-Nearest NeighborsRegression Using kNNClassification Using kNNSummary
The Titanic DatasetData Wranglingcaret UnleashedImputationData Splittingcaret Under the HoodModel TrainingComparing Multiple caret ModelsSummary

Content preview from Introduction to Machine Learning with R

Chapter 4. Regression in a Nutshell

In Chapter 1, in which we briefly explored the realms of machine learning, we began with linear regression because it is probably something that you have come across at some point in your mathematical training. The process is fairly intuitive and easier to explain as a first concept than some other machine learning models. Additionally, many realms of data analysis rely on regression modeling ranging from a business trying to forecast its profits, to the frontiers of science trying to figure out new discoveries governing the laws of the universe. We can find regression in any scenario in which a prediction against time is needed. In this chapter, we examine how to use regression modeling in R to a deep extent, but we also explore some caveats and pitfalls to be aware of in the process.

The main motivation behind regression is to build an equation by which we can learn more about our data. There is no hard-and-fast rule about which type of regression model to fit to your data, however. Choosing between a logistic regression, linear regression, or multivariate regression model depends on the problem and the data that you have. You could fit a straight line to a given series of data points, but is that always the best case? Ideally, we are after a balance of simplicity and explanatory power. A straight line fit to a complex series of data might be simple, but might not describe the whole picture. On the other hand, having a very simple set of data ...