Chapter 7 References
[1]
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The elements of statistical learning (12th printing). Springer New York.
[2]
Boehmke, B. and Greenwell, B. (2019). Hands-on machine learning with r.
[3]
Wolf, M. M. (2020). Lecture notes in mathematical foundations of supervised learning.
[4]
Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. CoRR abs/1603.02754.
[5]
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y. and Li, Y. (2021). Xgboost: Extreme gradient boosting.
[6]
Kuhn, M. and Wickham, H. (2020). Tidymodels: A collection of packages for modeling and machine learning using tidyverse principles.
[7]
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M., Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J., Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D., Wilke, C., Woo, K. and Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software 4 1686.
[8]
Garnier, S. (2018). Viridis: Default color maps from ’matplotlib’.
[9]
Wilke, C. O. (2020). Ggtext: Improved text rendering support for ’ggplot2’.
[10]
Garnier, S. (2018). viridisLite: Default color maps from ’matplotlib’ (lite version).
[11]
Pedersen, T. L. (2020). Patchwork: The composer of plots.
[12]
Tierney, N. (2017). Visdat: Visualising whole data frames. JOSS 2 355.
[13]
Grolemund, G. and Wickham, H. (2011). Dates and times made easy with lubridate. Journal of Statistical Software 40 1–25.
[14]
Meschiari, S. (2021). latex2exp: Use LaTeX expressions in plots.
[15]
Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., Elberg, A. and Crowley, J. (2021). GGally: Extension to ’ggplot2’.
[16]
Sievert, C. (2020). Interactive web-based data visualization with r, plotly, and shiny. Chapman; Hall/CRC.
[17]
Wright, M. N. and Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. Journal of Statistical Software 77 1–7.
[18]
Greenwell, B. M. and Boehmke, B. C. (2020). Variable importance plots—an introduction to the vip package. The R Journal 12 343–66.
[19]
Corporation, M. and Weston, S. (2019). doParallel: Foreach parallel adaptor for the ’parallel’ package.
[20]
Zhu, H. (2021). kableExtra: Construct complex table with ’kable’ and pipe syntax.