Predictive modelling

A form of supervised machine learning. Regression models are used to make predictions based on patterns in the data. This code supports the blog post Criminal goings-on in a random forest.

Citation

  1. Contains public sector information licensed under the Open Government Licence v3.0.
  2. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing,
    Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
  3. Max Kuhn and Ross Quinlan (2017). Cubist: Rule- And Instance-Based Regression Modeling. R package version 0.2.1. https://CRAN.R-project.org/package=Cubist
  4. Terry Therneau, Beth Atkinson and Brian Ripley (2017). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-11.
    https://CRAN.R-project.org/package=rpart
  5. Marvin N. Wright, Andreas Ziegler (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1-17. doi:10.18637/jss.v077.i01
  6. Greg Ridgeway with contributions from others (2017). gbm: Generalized Boosted Regression Models. R package version 2.1.3. https://CRAN.R-project.org/package=gbm
  7. Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt. (2017). caret: Classification and Regression Training. R package version 6.0-78. https://CRAN.R-project.org/package=caret