H2O | Encyclopedia MDPI

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1	handwiki	Camila Xu	--	1224	2022-11-30 01:45:52	\|
2	layout	Camila Xu	-541 word(s)	683	2022-11-30 03:14:50	\| \|
3	format correct	Jessie Wu	Meta information modification	683	2022-11-30 03:31:29	\|

H2O

The content is sourced from: https://handwiki.org/wiki/Software:H2O

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.

potential models open-source software h2o.ai

1. 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.

1.1. Mining of Big Data

Machine learning and data mining
https://handwiki.org/wiki/index.php?curid=2003206
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 Artificial 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
Artificial neural networks Autoencoder Deep learning DeepDream Multilayer perceptron RNN LSTM GRU Restricted Boltzmann machine GAN 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 NeurIPS 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
v t e

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.^[3]

H2O is also able to run on Spark.^[4]

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]^[3]^[5] 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,^[6] a parallel stochastic gradient method.^[7] 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.

1.2. Software

Programming languages

The H2O software has an interface to the following programming languages: Java (6 or later), Python (2.7.x, 3.5.x), R (3.0.0 or later) and Scala (1.4-1.6).^[2]^[8]

Operating systems

The H2O software can be run on conventional operating-systems: Microsoft Windows (7 or later), Mac OS X (10.9 or later), and Linux (Ubuntu 12.04 ; RHEL/CentOS 6 or later),^[8] It also runs on big-data systems, particularly Apache Hadoop Distributed File System (HDFS), several popular versions: Cloudera (5.1 or later), MapR (3.0 or later), and Hortonworks (HDP 2.1 or later). It also operates on cloud computing environments, for example using Amazon EC2, Google Compute Engine, and Microsoft Azure. The H2O Sparkling Water software is Databricks-certified on Apache Spark.^[8]

Graphical user interface and browsers

Its graphical user interface is compatible with four browsers (unless specified, in their latest versions (As of June 2015)): Chrome, Safari, Firefox, Internet Explorer (IE10).^[8]

References

(Harris 2012)
(Novet 2014)
Aiello, Spencer; Tom Kraljevic; Petr Maj (2015), h2o: R Interface for H2O, Contributed Packages, with contributions from the 0xdata team, The R Project for Statistical Computing, https://cran.r-project.org/web/packages/h2o/index.html
"FAQ — H2O 3.10.2.1 documentation" (in en). http://docs.h2o.ai/h2o/latest-stable/h2o-docs/faq.html#sparkling-water.
"Prediction of IncRNA using Deep Learning Approach". Tripathi, Rashmi; Kumari, Vandana; Patel, Sunil; Singh, Yashbir; Varadwaj, Pritish. International Conference on Advances in Biotechnology (BioTech). Proceedings: 138-142. Singapore: Global Science and Technology Forum. (2015)
Description of the iterative method for computing maximum-likelihood estimates for a generalized linear model. http://h2o-release.s3.amazonaws.com/h2o/rel-shannon/12/docs-website/h2o-docs/index.html#Data%20Science%20Algorithms-GLM-GLM%20Algorithm
Benjamin Recht; Re, Christopher; Wright, Stephen; Feng Niu (2011). J. Shawe-Taylor. ed. "Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent". Advances in Neural Information Processing Systems (Curran Associates, Inc.) 24: 693–701. http://papers.nips.cc/paper/4390-hogwild-a-lock-free-approach-to-parallelizing-stochastic-gradient-descent.pdf. Recht's PDF
"Recommended systems for H2O". H2O.ai. May 2015. http://0xdata.com/product/recommended-systems-for-h2o/.

©Text is available under the terms and conditions of the Creative Commons-Attribution ShareAlike (CC BY-SA) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Others

Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

HandWiki

View Times: 681

Entry Collection: HandWiki

Update Date: 30 Nov 2022

Table of Contents

Video Upload Options

Confirm