Introduction to Machine Learning with R

Index

Symbols

• ~ (function operator), R and Machine Learning, Regression

A

• a-priori probabilities, Application of Naive Bayes
• accuracy (classification benchmark), Training and Test Sets: Classification Modeling
• Adjusted R-squared value, Training and Testing of Data, Linear Regression
• advanced methods, Other Advanced Methods-Summary
  • k-nearest neighbors, k-Nearest Neighbors-Classification Using kNN
  • naive Bayes classification, Naive Bayes Classification-Application of Naive Bayes
  • principal component analysis, Principal Component Analysis-Linear Discriminant Analysis
  • support vector machines, Support Vector Machines-Support Vector Machines
• algorithms
  • defined, Algorithms Versus Models: What’s the Difference?, A Note on Terminology
  • in caret, Encyclopedia of Machine Learning Models in caret
  • models vs., Algorithms Versus Models: What’s the Difference?-Algorithms Versus Models: What’s the Difference?
• AND gate, Neural Networks-Neural Networks, Single-Layer Neural Networks

B

• Bayesian statistics
  • basics, Bayesian Statistics in a Nutshell
  • naive Bayes classification, Naive Bayes Classification-Application of Naive Bayes
• bias
  • in sampling, Bias-Bias
  • variance and, Bias
• bias node, Building a Simple Neural Network by Using R
• bias variable, Neural Networks
• binary classification, Binary Classification-Binary Classification

C

• call, Linear Regression
• caret package, Machine Learning with the caret Package-Summary
  • classification with, Classification
  • comparing multiple models, Comparing Multiple caret Models
  • data splitting, Data Splitting ...