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Specialty Support of Applications Critical to Innovation From commercial, open-source, and proprietary applications, to cloud-platforms, RCH Solutions is exclusively focused on supporting research computing and the software needed to move science forward. Bioinformatics Crystallography/ Design & Visualization & Genomics Cryo EM Visualization & Docking ABYSYS CCDC Abaqus/CAE FastROCS ArrayServer DivCon COMSOL Multiphysics Glide Bioconductor Phenix NX Nastran MOE CLC Bio SB Grid PyMol DNAseq, TNseq, Tnscope Spotfire Galaxy Molecular YASARA Genome Browser Data Science & Dynamics Tools & Management IMGT/HighV-QUEST Apache Spark Analytics AMBER Jchem Bright Cluster Manager Anaconda CHARMM Screener, Analyst, Expressionist, GCC Databricks Cloud DESMOND and Biologics Hadoop / MapReduce Jupyter Notebook GROMACS Ocimum technologies Kinetica NAMD Partek Flow IBM Spectrum LSF VMD Partek Genomics Suite PBS Pro RDKit Database SLURM EDEM Numerical Torque / Moab Computational PostgreSQL / MySQL Analytics Univa Grid Engine Oracle Suite MATLAB Chemistry & Synergy Mathematica Programming Biology SQLServer RStudio, Shiny, Connect Languages ActivityBase C / C++ Columbus Deep & Machine FORTRAN Discovery Studio Pharmacokinetics Learning Java / Scala GPU-Blast Mango perl Apache Mahout NONMEM MOPAC Python Apache MXNet Pirana / PiranaJS NVIBIO R Caffe2 PsN Pipeline Pilot Keras Ruby Schrodinger Suite MATLAB + Neural Network Spartian Toolbox Quantum Cloud Platforms WebCSD PyTorch Chemistry Tensorflow Amazon Web Services (AWS) Torch7 GAMESS Azure Computational Gaussian Google Cloud Platform (GCP) Fluid Dynamics Turbot Other ANSYS Fluent SilcsBio Particleworks Topspin Don’t see what you need? Our team’s specialized skill set enables a process for continuous education and mastery of new and evolving technology. Talk to us about your unique needs. 610-902-0400 | [email protected].

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Learning Apache Mahout Classification Table of Contents
Learning Apache Mahout Classification Table of Contents Learning Apache Mahout Classification Credits About the Author About the Reviewers www.PacktPub.com Support files, eBooks, discount offers, and more Why subscribe? Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1. Classification in Data Analysis Introducing the classification Application of the classification system Working of the classification system Classification algorithms Model evaluation techniques The confusion matrix The Receiver Operating Characteristics (ROC) graph Area under the ROC curve The entropy matrix Summary 2. Apache Mahout Introducing Apache Mahout Algorithms supported in Mahout Reasons for Mahout being a good choice for classification Installing Mahout Building Mahout from source using Maven Installing Maven Building Mahout code Setting up a development environment using Eclipse Setting up Mahout for a Windows user Summary 3. Learning Logistic Regression / SGD Using Mahout Introducing regression Understanding linear regression Cost function Gradient descent Logistic regression Stochastic Gradient Descent Using Mahout for logistic regression Summary 4. Learning the Naïve Bayes Classification Using Mahout Introducing conditional probability and the Bayes rule Understanding the Naïve Bayes algorithm Understanding the terms used in text classification Using the Naïve Bayes algorithm in Apache Mahout Summary 5. Learning the Hidden Markov Model Using Mahout Deterministic and nondeterministic patterns The Markov process Introducing the Hidden Markov Model Using Mahout for the Hidden Markov Model Summary 6. Learning Random Forest Using Mahout Decision tree Random forest Using Mahout for Random forest Steps to use the Random forest algorithm in Mahout Summary 7.
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