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 Text Features TF-IDF Tokenizer BM25 Image Features Deep Feature Extractor Other Transformations Hasher Transformer Chain Custom Transformer Modeling data Graph analytics Examples Regression Linear Regression Random Forest Regression Boosted Trees Regression Classification Logistic Regression Nearest Neighbor Classifier SVM Random Forest Classifier Boosted Trees Classifier Neuralnet Classifier 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 Choosing a model Making recommendations Finding similar items Data matching Record Linker Deduplication Autotagger Similarity Search Churn prediction 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 Dato Distributed Asynchronous Jobs Installing on Hadoop Clusters End-to-End Example Distributed Job Execution Distributed Machine Learning Monitoring Jobs Session Management Dependencies Predictive Services Getting Started Launching Querying Predictive Objects Logging and Feedback Dependencies Experimentation Operations Monitoring and Metrics Administration Best Practices Run On-Premises Conclusion Exercises Tabular data Graph data Graph analytics Classification Text analysis Recommender systems FAQ/Common Problems Contributing