H2O makes it possible for anyone to easily apply machine learning and predictive analytics to solve today’s most challenging business problems. It intelligently combines the following features:
- Best of Breed Open Source Technology – Enjoy the freedom that comes with big data science powered by open source technology. H2O was written from scratch in Java and seamlessly integrates with the most popular open source products like Apache Hadoop® and Spark™.
- Easy-to-use WebUI and Familiar Interfaces – Set up and get started quickly using either H2O’s intuitive web-based Flow GUI or familiar programming environments like R, Python, Java, Scala, JSON, and through our powerful APIs. Models can be visually inspected during training.
- Data Agnostic Support for all Common Database and File Types – Easily explore and model big data from within Microsoft Excel, R Studio, Tableau and more. Connect to data from HDFS, S3, SQL and NoSQL data sources. Install and deploy anywhere, in the cloud, on premise, on workstations, servers or clusters.
- Massively Scalable Big Data Munging and Analysis – H2O Big Joins performs 7x faster than R data.table in a benchmark, and linearly scales to 10 billion x 10 billion row joins. Train a model on complete data sets, not just small samples, and iterate and develop models in real-time with H2O’s rapid in-memory distributed parallel processing.
- Real-time Data Scoring – Rapidly deploy models to production via plain-old Java objects (POJO), model-optimized Java objects (MOJO) or REST API. Score new data against models for accurate and fast predictions in any environment.
H2O supports the following distributed algorithms:
- Statistical Analysis
- Generalized Linear Models
- Naïve Bayes
- Distributed Random Forest
- Gradient Boosting Machine
- Deep Neural Networks
- Deep Learning
- Dimensionality Reduction
- Principal Component Analysis
- Generalized Low Rank Models
- Anomaly Detection
Advanced Features for Data Scientists
- Automatic standardization of the predictors
- Automatic initialization of model parameters (weights & biases)
- Automatic adaptive learning rates
- Ability to specify manual learning rate with annealing and momentum
- Automatic handling of categorical and missing data
- Regularization techniques to manage complexity
- Automatic parameter optimization with grid search
- Early stopping based on validation datasets
- Automatic cross-validation
- Go to h2o.ai/downloads. Download H2O. This is a zip file that contains everything you need to get started.
- From your terminal, run: cd ~/Downloads unzip h2o-184.108.40.206.zip cd h2o-220.127.116.11 java -jar h2o.jar
- Point your browser to http://localhost:54321