H2O (software)

H2O

Original author(s)	H2O.ai
Developer(s)	H2O.ai
Initial release	2011 (2011)
Stable release	3.20.0.8 / August 1, 2018 (2018-08-01)
Repository	https://github.com/h2oai/h2o-3
Written in	H2O (written in Java, Python, and R)[1][2][3]
Operating system	Linux, macOS, and Microsoft Windows
Platform	Apache Hadoop Distributed File System; Amazon EC2, Google Compute Engine, and Microsoft Azure.
Standard(s)	Databricks certified on Spark.[3]
Available in	English
Type	big data analytics, machine learning, statistical learning theory[4]
License	Apache license 2.0[5]
Website	www.h2o.ai
As of	1 June 2015

H2O is open-source software for big-data analysis. It is produced by the company H2O.ai. H2O allows users to fit thousands of potential models as part of discovering patterns in data.

The H2O software runs can be called from the statistical package R, Python, and other environments. It is used for exploring and analyzing datasets held in cloud computing systems and in the Apache Hadoop Distributed File System as well as in the conventional operating-systems Linux, macOS, and Microsoft Windows. The H2O software is written in Java, Python, and R. Its graphical-user interface is compatible with four browsers: Chrome, Safari, Firefox, and Internet Explorer.

H2O

The H2O project aims to develop an analytical interface for cloud computing, providing users with tools for data analysis.^[1] The software is open-source and freely distributed. The company receives fees for providing customer service and customized extensions.

Mining of big data

See also: Data mining and Machine learning

Machine learning and data mining
Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction
Supervised learning (classification • regression) Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Expectation–maximization (EM) DBSCAN OPTICS Mean-shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA t-SNE
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection k-NN Local outlier factor
Neural nets Autoencoder Deep learning Multilayer perceptron RNN LSTM GRU) Restricted Boltzmann machine SOM Convolutional neural network U-Net
Reinforcement learning Q-learning SARSA Temporal difference (TD)
Theory Bias-variance dilemma Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues NIPS ICML ML JMLR ArXiv:cs.LG
Glossary of artificial intelligence Glossary of artificial intelligence
Related articles List of datasets for machine-learning research Outline of machine learning
Machine learning portal

Big datasets are too large to be analyzed using traditional software like R. The H2O software provides data structures and methods suitable for big data. H2O allow users to analyze and visualize whole sets of data without using the Procrustean strategy of studying only a small subset with a conventional statistical package.^[2] H2O's statistical algorithms includes K-means clustering, generalized linear models, distributed random forests, gradient boosting machines, naive bayes, principal component analysis, and generalized low rank models.^[6]

H2O is also able to run on Spark.^[7]

Iterative methods for real-time problems

H2O uses iterative methods that provide quick answers using all of the client's data. When a client cannot wait for an optimal solution, the client can interrupt the computations and use an approximate solution.^[1] In its approach to deep learning,^[2][6][8] H2O divides all the data into subsets and then analyzing each subset simultaneously using the same method. These processes are combined to estimate parameters by using the Hogwild scheme,^[9] a parallel stochastic gradient method.^[10] These methods allow H2O to provide answers that use all the client's data, rather than throwing away most of it and analyzing a subset with conventional software.

Notes

References

• Gage, Deborah (15 April 2013). "Platfora founder goes in search of big-data answers". Wall Street Journal. Retrieved 2 June 2015.
• Hackett, Robert (3 August 2014), Nusca, Andrew; Hackett, Robert; Gupta, Shalene, eds., "Arno Candel, physicist and hacker, 0xdata", Fortune, Meet Fortune's 2014 Big Data All-Stars, retrieved 2 June 2015
• Hardy, Quentin (3 May 2014). "Valuable humans in our digital future". New York Times. Retrieved 1 June 2015.
• Harris, Derrick (14 August 2012). "How 0xdata wants to help everyone become data scientists". Gigaom Research. Retrieved 1 June 2015.
• Novet, Jordan (7 November 2014). "0xdata takes $8.9M and becomes H2O to match its open-source machine-learning project". VentureBeat. Retrieved 1 June 2015.

External links

• H2O software at H2O.ai (formerly 0xdata)
• Github repository of H2O software
• YouTube channel of H2O.ai