20.18 Summary of the Model

summary(model)
## Call:
## rpart(formula=form, data=ds[tr, vars], model=TRUE)
##   n= 158807 
## 
##           CP nsplit rel error    xerror        xstd
## 1 0.15356046      0 1.0000000 1.0000000 0.004790312
## 2 0.03491739      1 0.8464395 0.8461179 0.004498424
## 3 0.01000000      3 0.7766048 0.7792660 0.004354886
## 
## Variable importance
## humidity_3pm     rainfall     temp_3pm     sunshine   rain_today     max_temp 
##           78            4            4            4            3            2 
## humidity_9am    cloud_3pm 
##            2            1 
## 
## Node number 1: 158807 observations,    complexity param=0.1535605
##   predicted class=No   expected loss=0.2153243  P(node) =1
##     class counts: 124612 34195
##    probabilities: 0.785 0.215 
##   left son=2 (132754 obs) right son=3 (26053 obs)
##   Primary splits:
##       humidity_3pm < 71.5    to the left,  improve=9260.796, (0 missing)
##       rainfall     < 0.75    to the left,  improve=5492.700, (0 missing)
##       rain_today   splits as  LR,          improve=5467.632, (0 missing)
##       cloud_3pm    < 6.5     to the left,  improve=3904.979, (0 missing)
##       humidity_9am < 74.5    to the left,  improve=3015.951, (0 missing)
##   Surrogate splits:
##       sunshine  < 0.85    to the right, agree=0.844, adj=0.051, (0 split)
##       temp_3pm  < 10.75   to the right, agree=0.844, adj=0.048, (0 split)
##       max_temp  < 10.95   to the right, agree=0.840, adj=0.026, (0 split)
##       rainfall  < 30.3    to the left,  agree=0.839, adj=0.018, (0 split)
##       cloud_3pm < 7.5     to the left,  agree=0.837, adj=0.005, (0 split)
## 
## Node number 2: 132754 observations
##   predicted class=No   expected loss=0.1396794  P(node) =0.8359455
##     class counts: 114211 18543
##    probabilities: 0.860 0.140 
## 
## Node number 3: 26053 observations,    complexity param=0.03491739
##   predicted class=Yes  expected loss=0.3992247  P(node) =0.1640545
....

In the following pages we dissect the various components of this summary.



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