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Machine Learning with R

You're reading from   Machine Learning with R Expert techniques for predictive modeling

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788295864
Length 458 pages
Edition 3rd Edition
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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 (16) 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 Other Books You May Enjoy
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Index

Index

A

  • activation function / From biological to artificial neurons, Activation functions
  • AdaBoost / Boosting
  • AdaBoost.M1 algorithm / Boosting
  • adaptive boosting / Boosting the accuracy of decision trees, Boosting
  • adversarial learning / Types of machine learning algorithms
  • algorithms
    • input data, matching to / Matching input data to algorithms
  • allocation function
    • about / Understanding ensembles
  • Amazon Web Services (AWS) / Step 5 – improving model performance
  • ANNs, used for modeling concrete strength
    • about / Example – modeling the strength of concrete with ANNs
    • data collection / Step 1 – collecting data
    • data exploration / Step 2 – exploring and preparing the data
    • data preparation / Step 2 – exploring and preparing the data
    • model, training on data / Step 3 – training a model on the data
    • model performance, evaluating / Step 4 – evaluating model performance...
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