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.

Read now

Unlock full access

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 3. Sampling Statistics and Model Training in R

Sampling and machine learning go hand in hand. In machine learning, we typically begin with a big dataset that we want to use for predicting something. We usually split this data into a training set and build a model around that, and then unleash a fully trained model on some kind of test set to see what the final output is. In some instances, it might be very difficult to run a machine learning model on an entire dataset, whereas we might achieve as good an accuracy by running on a small sample of it and testing when appropriate. This could be due to the size of the data, for example.

First let’s define some statistical terms. A population is the entire collection (or universe) of things under consideration. A sample is a portion of the population that we select for analysis. So, for example, we could start with a full dataset, break off a chunk into a sample, and do our training there. Another way to look at it is that some data that we’re given to start with might itself be only a sample of a much broader dataset.

Polling data is an example of sampling, and is typically gathered by asking questions of people for specific demographics. By design, the polling data can be only a subset of the general population of a country, because it would be quite an achievement to ask everyone in a country what their favorite color might be. If we have a country with a population of 100 million and we conduct a poll that has 30 million ...