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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling to solve all your data analysis problems

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Product type Paperback
Published in Jul 2015
Publisher Packt
ISBN-13 9781784393908
Length 452 pages
Edition 2nd Edition
Languages
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Author (1):
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Brett Lantz Brett Lantz
Author Profile Icon Brett Lantz
Brett Lantz
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Machine Learning 2. Managing and Understanding Data FREE CHAPTER 3. Lazy Learning – Classification Using Nearest Neighbors 4. Probabilistic Learning – Classification Using Naive Bayes 5. Divide and Conquer – Classification Using Decision Trees and Rules 6. Forecasting Numeric Data – Regression Methods 7. Black Box Methods – Neural Networks and Support Vector Machines 8. Finding Patterns – Market Basket Analysis Using Association Rules 9. Finding Groups of Data – Clustering with k-means 10. Evaluating Model Performance 11. Improving Model Performance 12. Specialized Machine Learning Topics Index

Index

A

  • abstraction / Abstraction
  • activation function / From biological to artificial neurons
    • about / Activation functions
    • threshold activation function / Activation functions
    • unit step activation function / Activation functions
    • sigmoid activation function / Activation functions
  • AdaBoost
    • about / Boosting
  • AdaBoost.M1 algorithm / Boosting
  • adaptive boosting
    • about / Boosting the accuracy of decision trees, Boosting
  • allocation function / Understanding ensembles
  • Apache Hadoop
    • about / Parallel cloud computing with MapReduce and Hadoop
  • Application Programming Interfaces (APIs)
    • about / Parsing JSON from web APIs
  • Apriori
    • property / The Apriori algorithm for association rule learning
  • Apriori algorithm
    • for association rule learning / The Apriori algorithm for association rule learning
    • strengths / The Apriori algorithm for association rule learning
  • Apriori principle
    • used, for building set of rules / Building...
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