Summary This is the User Guide for GraphLab Create Getting started Working with data Tabular data Loading and Saving Data Manipulation Spark RDDs SQL Databases Graph data Time series data Visualization Feature Engineering Numeric Features Quadratic Features Feature Binning Numeric Imputer Categorical Features One Hot Encoder Count Thresholder Categorical Imputer Count Featurizer Text Features TF-IDF Tokenizer RareWordTrimmer BM25 PartOfSpeechExtractor SentenceSplitter Image Features Deep Feature Extractor Other Transformations Hasher Random Projection Transformer Chain Custom Transformer Modeling data Classification Logistic Regression Nearest Neighbor Classifier SVM Decision Tree Classifier Random Forest Classifier Boosted Trees Classifier Neuralnet Classifier Regression Linear Regression Decision Tree Regression Boosted Trees Regression Random Forest Regression Advanced Deep Learning with MXNet Graph analytics Examples Clustering KMeans DBSCAN Nearest Neighbors Text analysis Processing text Topic models Evaluating Models Regression Metrics Classification Metrics Model parameter search Models Choosing a search space Evaluation functions Distributed execution Applications Recommender systems Using trained models Choosing a model Data matching Record Linker Deduplication Autotagger Similarity Search Lead Scoring Churn prediction Using a trained model Alternate input formats How it works Frequent Pattern Mining Sentiment analysis Applying a sentiment classifier Product sentiment analysis and review data Anomaly Detection Local Outlier Factor Moving Z-Score Bayesian Changepoints Turi Distributed Asynchronous Jobs Installing on Hadoop Clusters End-to-End Example Distributed Job Execution Distributed Machine Learning Monitoring Jobs Session Management Dependencies Turi Predictive Services Conclusion Exercises Tabular data Graph data Graph analytics Classification Text analysis Recommender systems FAQ/Common Problems Contributing