20.82 Conditional Regression Tree
We can also build a regression tree using (Hothorn et al. 2025).
##
## Model formula:
## risk_mm ~ rain_today + temp_3pm + temp_9am + cloud_3pm + cloud_9am +
## pressure_3pm + pressure_9am + humidity_3pm + humidity_9am +
## wind_speed_3pm + wind_speed_9am + wind_dir_3pm + wind_dir_9am +
## wind_gust_speed + wind_gust_dir + sunshine + evaporation +
## rainfall + max_temp + min_temp
##
## Fitted party:
## [1] root
## | [2] rainfall <= 21.2
## | | [3] humidity_3pm <= 82
## | | | [4] humidity_3pm <= 66
## | | | | [5] pressure_3pm <= 1008.9
## | | | | | [6] humidity_3pm <= 51
## | | | | | | [7] sunshine <= 7.9
## | | | | | | | [8] cloud_3pm <= 6
## | | | | | | | | [9] pressure_3pm <= 997: 5.208 (n=62, err=2777.8)
## | | | | | | | | [10] pressure_3pm > 997: 2.457 (n=833, err=NA)
## | | | | | | | [11] cloud_3pm > 6
## | | | | | | | | [12] wind_dir_3pm <= NNE: 7.304 (n=150, err=NA)
## | | | | | | | | [13] wind_dir_3pm > NNE: 3.060 (n=593, err=NA)
## | | | | | | [14] sunshine > 7.9
## | | | | | | | [15] humidity_3pm <= 31
## | | | | | | | | [16] wind_gust_speed <= 56
## | | | | | | | | | [17] humidity_3pm <= 18: 0.182 (n=1833, err=NA)
## | | | | | | | | | [18] humidity_3pm > 18
## | | | | | | | | | | [19] sunshine <= 8.3: 4.600 (n=20, err=3803.5)
## | | | | | | | | | | [20] sunshine > 8.3
## | | | | | | | | | | | [21] sunshine <= 10.5: 0.742 (n=1411, err=NA)
## | | | | | | | | | | | [22] sunshine > 10.5
## | | | | | | | | | | | | [23] wind_dir_3pm <= NNE: 1.446 (n=39, err=701.2)
## | | | | | | | | | | | | [24] wind_dir_3pm > NNE: 0.052 (n=511, err=NA)
## | | | | | | | | [25] wind_gust_speed > 56
## | | | | | | | | | [26] humidity_3pm <= 17
## | | | | | | | | | | [27] wind_gust_speed <= 74
## | | | | | | | | | | | [28] humidity_9am <= 74: 0.412 (n=560, err=NA)
## | | | | | | | | | | | [29] humidity_9am > 74: 3.086 (n=7, err=358.7)
## | | | | | | | | | | [30] wind_gust_speed > 74: 1.147 (n=135, err=1211.3)
## | | | | | | | | | [31] humidity_3pm > 17
## | | | | | | | | | | [32] temp_3pm <= 27
## | | | | | | | | | | | [33] pressure_3pm <= 995.2: 4.000 (n=15, err=303.5)
## | | | | | | | | | | | [34] pressure_3pm > 995.2
## | | | | | | | | | | | | [35] wind_dir_9am <= ESE: 1.370 (n=114, err=NA)
## | | | | | | | | | | | | [36] wind_dir_9am > ESE
....
References
Hothorn, Torsten, Kurt Hornik, Carolin Strobl, and Achim Zeileis. 2025. Party: A Laboratory for Recursive Partytioning. http://party.R-forge.R-project.org.
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0