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Game Data Science Using R
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Getting Started
Why Game Data Science?
Introduction to Game Data Science
Game AnalyticsWhat is Game Data?Historical Context for Game Data ScienceEra of Game Data and Machine LearningThe Process of Game Data ScienceWhat are Metrics in Game Data Science?Introduction to Customer MetricsIntroduction to Gameplay MetricsKey Performance IndicatorsImportance of Metrics to Game Data ScienceSummary: Basics of Game Data ScienceQuiz on Basics of Game Data Science
Data Preprocessing
About This ChapterWhat is Data Preprocessing?Data Example and Measurement TypesProcess for PreprocessingReading and Parsing FilesPurpose of Cleaning and Data Type ChecksData Consistency ProcessingData Normalization and StandardizationSummary: Data reprocessingQuiz on Preprocessing of Data
Introduction to Statistics and Probability Theory
Introduction to Probability and StatisticsMeasures of CentralityMeasure of SpreadIntroduction to Correlation AnalysisIntroduction to Inferential StatisticsT-tests and its TypesIntroduction to Analysis of Variance (ANOVA)Introduction to ProbabilitySummary: Statistics and Probability TheoryQuiz on Statistics and Probability Theory
Data Abstraction
Significance of Behavioral TelemetryIntroduction to DatasetIntroduction to Feature ExtractionHow to Deal with Nominal and Ordinal Measures?The Process of Feature SelectionIntroduction to EntropySummary: Data AbstractionQuiz on Data Abstraction
Data Analysis through Visualization
Overview of the ChapterIntroduction to HeatmapsSpatio-Temporal Visualization SystemsExploring Spatio-Temporal Player Behavior in Dota 2State-action Visualization SystemsUsing State-action Transition to Compare Players’ ChoicesExploring Players' Dialogue Choices (Lab)Summary: Data Analysis Through VisualizationQuiz on Data Analysis Through Visualization
Clustering Methods in Game Data Science
Introduction to ClustersWhat is Cluster Analysis?Clustering ModelsThe Clustering ProcessChallenges in Applying the Clustering ProcessEvaluating and Tuning the ResultsIntroduction to The K-means AlgorithmAn Example of K-Means From the Wild: Tera OnlineThe k-Means labIntroduction to Fuzzy C-MeansFuzzy C-Means LabBasics of DBSCANDBSCAN LabIntroduction to Hierarchical Clustering MethodsHierarchical Clustering LabIntroduction to Archetypal Analysis (AA)Archetypal Analysis LabIntroduction to Model-based ClusteringAdvice on Applying Cluster MethodsSummary: Clustering Methods in Game Data ScienceQuiz on Clustering Methods
Supervised Learning in Game Data Science
Introduction to Machine learningSupervised Machine LearningWays to Categorize Prediction ModelsIntroduction to Regression MethodsLinear Regression LabIntroduction to ClassificationK-nearest-Neighbour(KNN) LabIntroduction to Naive Bayes TheoremNaive Bayes LabIntroduction to Logistics RegressionLogistic Regression LabIntroduction to Linear Discriminant Analysis (LDA)Linear Discriminant Analysis (LDA) LabIntroduction to Support Vector Machines (SVMs)Support Vector Machine LabKernel Methods with Support Vector MachinesLab of Support Vector Machines with Kernel MethodsIntroduction to Decision TreesDecision Tree LabIntroduction to Random ForestsRandom Forests LabSummary: Supervised Learning in Game Data ScienceQuiz on Supervised Learning
Model Validation and Evaluation
Introduction to Machine Learning Pipeline
How to Determine if the Model Is Good or Bad
Lab of Confusion Matrix
Introduction to Area Under the Curve (AUC)
Introduction to Regression Metrics
Regression Metrics Lab
What is Model Validation Process?
Cross-Validation Lab
How to Debug a Model That Is Already Trained
Summary: Model Validation and Evaluation
Quiz on Model Validation and Evaluation
Introduction to Neural Networks
What are Neural Networks?What is a Feedforward Neural Network?Working of a Feedforward Neural NetworkHow to Train Feedforward Neural NetworksUsage of Deep Neural Networks in Game Data ScienceIntroduction to Convolutional Neural NetworkHow Does the Convolutional Layer Work?Usage of CNNs in Game Data ScienceSummary: Neural NetworksQuiz on the Neural Networks
Sequence Analysis of Game Data
Introduction to Sequence AnalysisWhy Sequence Data Analysis?Sequence Mining Enables Player Profiling: A Case StudyDota 2 DataExploratory Sequence Data AnalysisInspecting Frequent Patterns in Explorative AnalysisThe Entropy of States in Explorative AnalysisLab of Exploratory Sequence Data AnalysisIntroduction to Sequence Pattern MiningLab for SPADEIntroduction to Clustering of SequencesLab of Clustering SequencesSummary: Sequence Analysis of Game DataQuiz on Sequence Analysis of Game Data
Advanced Sequence Analysis
Introduction to Advanced Sequence AnalysisProbabilistic Planning-Based ApproachIntroduction to PHATT planning approachIntroduction to Bayesian NetworksWhat are Hidden Markov Models (HMMs)?Introduction to Markov Decision Process (MDP)Usage of the Markov Decision Process in GamesExtension of Markov Decision ProcessIntroduction to Markov Logic Networks (MLNs)Introduction to Recurrent Neural Networks (RNNs)Usage of Recurrent Neural Networks (RNNs) in GamesSummary: Advanced Sequence Data AnalysisQuiz: Advanced Sequence Analysis
Case Study: Tom Clancy's The Division (TCTD)
Introduction to the Case StudyGame: Tom Clancy’s The Division (TCTD)Data Analysis in the Case StudyResults of the Case StudyDiscussion and Conclusion of the Case Study
Conclusion and Remarks
Game Data Science ProcessOther Topics in Game Data ScienceCopyright Information
Appendix A: Game Used in the Book
VPAL Game

Cross-Validation Lab

Lets code cross-validation in this lab.

We'll cover the following
  • Goal
  • Brief refresher
  • Step 1: Load the data
  • Step 2: Find out which parameters are tunable
  • Step 3: Set up a grid of parameter configurations
  • Step 4: Set up cross-validation
  • Step 5: Train the model with cross validation and parameter grid
  • Step 6 (optional): Plot the accuracy of the models
  • Step 7: Apply the model to the test set
  • Conclusion

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