book

Practical Machine Learning with R

by Brindha Priyadarshini Jeyaraman, Ludvig Renbo Olsen, Monicah Wambugu

August 2019

Beginner to intermediate

416 pages

7h 5m

English

Packt Publishing

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About the BookAbout the AuthorsDescriptionLearning ObjectivesAudienceApproachMinimum Hardware RequirementsSoftware RequirementsConventionsInstallation and SetupInstalling RInstalling R StudioInstalling LibrariesInstalling the Code BundleAdditional Resources
An Introduction to Machine LearningIntroductionThe Machine Learning ProcessRaw DataData Pre-ProcessingThe Data Splitting ProcessThe Training ProcessEvaluation ProcessDeployment ProcessProcess Flow for Making Predictions Introduction to RExercise 1: Reading from a CSV File in RStudioExercise 2: Performing Operations on a DataframeExploratory Data Analysis (EDA)View Built-in Datasets in RExercise 3: Loading Built-in DatasetsExercise 4: Viewing Summaries of DataVisualizing the DataActivity 1: Finding the Distribution of Diabetic Patients in the PimaIndiansDiabetes DatasetActivity 2: Grouping the PimaIndiansDiabetes DataActivity 3: Performing EDA on the PimaIndiansDiabetes DatasetMachine Learning ModelsTypes of PredictionSupervised LearningUnsupervised LearningApplications of Machine LearningRegressionExercise 5: Building a Linear Classifier in RActivity 4: Building Linear Models for the GermanCredit DatasetActivity 5: Using Multiple Variables for a Regression Model for the Boston Housing DatasetSummary
Data Cleaning and Pre-processingIntroductionAdvanced Operations on Data FramesExercise 6: Sorting the Data FrameJoin OperationsPre-Processing of Data FramesExercise 7: Centering VariablesExercise 8: Normalizing the VariablesExercise 9: Scaling the VariablesActivity 6: Centering and Scaling the VariablesExtracting the Principle ComponentsExercise 10: Extracting the Principle ComponentsSubsetting DataExercise 11: Subsetting a Data FrameData TransposesIdentifying the Input and Output VariablesIdentifying the Category of PredictionHandling Missing Values, Duplicates, and OutliersHandling Missing ValuesExercise 12: Identifying the Missing ValuesTechniques for Handling Missing ValuesExercise 13: Imputing Using the MICE PackageExercise 14: Performing Predictive Mean MatchingHandling DuplicatesExercise 15: Identifying DuplicatesTechniques Used to Handle Duplicate ValuesHandling OutliersExercise 16: Identifying Outlier ValuesTechniques Used to Handle OutliersExercise 17: Predicting Values to Handle OutliersHandling Missing DataExercise 18: Handling Missing ValuesActivity 7: Identifying OutliersPre-Processing Categorical DataHandling Imbalanced DatasetsUndersamplingExercise 19: Undersampling a DatasetOversamplingExercise 20: OversamplingROSEExercise 21: Oversampling using ROSESMOTEExercise 22: Implementing the SMOTE TechniqueActivity 8: Oversampling and Undersampling using SMOTEActivity 9: Sampling and Oversampling using ROSESummary
Feature EngineeringIntroductionTypes of FeaturesDatatype-Based FeaturesDate and Time FeaturesExercise 23: Creating Date FeaturesExercise 24: Creating Time FeaturesTime Series FeaturesExercise 25: BinningActivity 10: Creating Time Series Features – BinningSummary StatisticsExercise 26: Finding Description of FeaturesStandardizing and RescalingHandling Categorical VariablesSkewnessExercise 27: Computing SkewnessActivity 11: Identifying SkewnessReducing Skewness Using Log TransformExercise 28: Using Log TransformDerived Features or Domain-Specific FeaturesAdding Features to a Data FrameExercise 29: Adding a New Column to an R Data FrameHandling Redundant FeaturesExercise 30: Identifying Redundant FeaturesText Features Exercise 31: Automatically Generating Text Features Feature SelectionCorrelation AnalysisExercise 32: Plotting Correlation between Two VariablesP-ValueExercise 33: Calculating the P-ValueRecursive Feature EliminationExercise 34: Implementing Recursive Feature EliminationPCAExercise 35: Implementing PCA Activity 12: Generating PCARanking FeaturesVariable Importance Approach with Learning Vector QuantizationExercise 36: Implementing LVQVariable Importance Approach Using Random ForestsExercise 37: Finding Variable Importance in the PimaIndiansDiabetes DatasetActivity 13: Implementing the Random Forest ApproachVariable Importance Approach Using a Logistic Regression ModelExercise 38: Implementing the Logistic Regression ModelDetermining Variable Importance Using rpartExercise 39: Variable Importance Using rpart for the PimaIndiansDiabetes DataActivity 14: Selecting Features Using Variable ImportanceSummary
Introduction to neuralnet and Evaluation MethodsIntroductionClassificationBinary ClassificationExercise 40: Preparing the DatasetBalanced Partitioning Using the groupdata2 PackageExercise 41: Partitioning the DatasetExercise 42: Creating Balanced PartitionsLeakageExercise 43: Ensuring an Equal Number of Observations Per ClassStandardizingNeural Networks with neuralnetActivity 15: Training a Neural NetworkModel SelectionEvaluation MetricsAccuracyPrecisionRecallExercise 44: Creating a Confusion MatrixExercise 45: Creating Baseline EvaluationsOver and UnderfittingAdding Layers and Nodes in neuralnetCross-ValidationCreating FoldsExercise 46: Writing a Cross-Validation Training LoopActivity 16: Training and Comparing Neural Network ArchitecturesActivity 17: Training and Comparing Neural Network Architectures with Cross-ValidationMulticlass Classification OverviewSummary
Linear and Logistic Regression ModelsIntroductionRegressionLinear RegressionExercise 47: Training Linear Regression ModelsR2Exercise 48: Plotting Model PredictionsExercise 49: Incrementally Adding PredictorsComparing Linear Regression ModelsEvaluation MetricsMAERMSEDifferences between MAE and RMSEExercise 50: Comparing Models with the cvms PackageInteractionsExercise 51: Adding Interaction Terms to Our ModelShould We Standardize Predictors?Repeated Cross-ValidationExercise 52: Running Repeated Cross-ValidationExercise 53: Validating Models with validate()Activity 18: Implementing Linear RegressionLog-Transforming PredictorsExercise 54: Log-Transforming PredictorsLogistic RegressionExercise 55: Training Logistic Regression ModelsExercise 56: Creating Binomial Baseline Evaluations with cvmsExercise 57: Creating Gaussian Baseline Evaluations with cvmsRegression and Classification with Decision TreesExercise 58: Training Random Forest ModelsModel Selection by Multiple Disagreeing MetricsPareto DominanceExercise 59: Plotting the Pareto FrontActivity 19: Classifying Room TypesSummary
Unsupervised LearningIntroductionOverview of Unsupervised Learning (Clustering)Hard versus Soft ClustersFlat versus Hierarchical ClusteringMonothetic versus Polythetic ClusteringExercise 60: Monothetic and Hierarchical Clustering on a Binary DatasetDIANAExercise 61: Implement Hierarchical Clustering Using DIANAAGNESExercise 62: Agglomerative Clustering Using AGNESDistance Metrics in ClusteringExercise 63: Calculate Dissimilarity Matrices Using Euclidean and Manhattan DistanceCorrelation-Based Distance MetricsExercise 64: Apply Correlation-Based MetricsApplications of Clusteringk-means ClusteringExploratory Data Analysis Using Scatter PlotsThe Elbow MethodExercise 65: Implementation of k-means Clustering in RActivity 20: Perform DIANA, AGNES, and k-means on the Built-In Motor Car DatasetSummary
Chapter 1: An Introduction to Machine LearningActivity 1: Finding the Distribution of Diabetic Patients in the PimaIndiansDiabetes DatasetActivity 2: Grouping the PimaIndiansDiabetes DataActivity 3: Performing EDA on the PimaIndiansDiabetes DatasetActivity 4: Building Linear Models for the GermanCredit DatasetActivity 5: Using Multiple Variables for a Regression Model for the Boston Housing DatasetChapter 2: Data Cleaning and Pre-processingActivity 6: Pre-processing using Center and ScaleActivity 7: Identifying OutliersActivity 8: Oversampling and UndersamplingActivity 9: Sampling and OverSampling using ROSESolution:Chapter 3: Feature EngineeringActivity 10: Calculating Time series Feature – BinningActivity 11: Identifying SkewnessActivity 12: Generating PCAActivity 13: Implementing the Random Forest ApproachActivity 14: Selecting Features Using Variable ImportanceChapter 4: Introduction to neuralnet and Evaluation MethodsActivity 15: Training a Neural NetworkActivity 16: Training and Comparing Neural Network ArchitecturesActivity 17: Training and Comparing Neural Network Architectures with Cross-ValidationChapter 5: Linear and Logistic Regression ModelsActivity 18: Implementing Linear RegressionActivity 19: Classifying Room TypesChapter 6: Unsupervised LearningActivity 20: Perform DIANA, AGNES, and k-means on the Built-In Motor Car Dataset

Content preview from Practical Machine Learning with R

Chapter 6 Unsupervised Learning

Learning Objectives

By the end of this chapter, you will be able to:

Distinguish between unsupervised and supervised learning
Implement different techniques applied in clustering, such as soft and hard clustering, monothetic and polythetic clustering, and bottom-up versus top-down clustering
Perform k-means clustering
Compare performance using DIANA, AGNES, and k-means

In this chapter, we aim to equip you with a practical understanding of unsupervised learning.

Introduction

In this chapter, we will look at the implementation of unsupervised learning. We will explore different ways of clustering; namely, bottom-up (or agglomerative) and top-down (or divisive). We will also look at the distinction between ...