Dear All,
I am fine tuning a Cubist model (see
https://cran.r-project.org/web/packages/Cubist/index.html).
I am a bit puzzled by its output. On a dataset which contains 275
cases, I get non mutually exclusive rules.
E.g., in the output below, rules 2 and 3 cover all the 275 cases of
the data set and rule 1 overlaps partially.
Am I misunderstanding something?
Many thanks
Lorenzo
Cubist [Release 2.07 GPL Edition] Thu Jan 12 23:10:40 2017
---------------------------------
Target attribute `outcome'
Read 275 cases (21 attributes) from undefined.data
Model:
Rule 1: [204 cases, mean 0.5393324, range 0 to 2.285714, est err
0.2598495]
if
home_copub_after_all <= 0.7142857
host_copub_after_all <= 1.833333
then
outcome = 0.1666667 + 0.9 home_copub_after_all
+ 0.11 home_copub_before_all
Rule 2: [259 cases, mean 0.7445303, range 0 to 3.166667, est err
0.1866440]
if
host_copub_after_all <= 1.833333
then
outcome = 0.0433333 + 0.75 home_copub_after_all
+ 0.33 host_copub_after_all + 0.37
top_10_after_all
Rule 3: [16 cases, mean 4.4285712, range 2.142857 to 8.857142, est
err 1.0346190]
if
host_copub_after_all > 1.833333
then
outcome = 1.595 + 1.03 top_10_after_all + 0.45
home_copub_after_all
Evaluation on training data (275 cases):
Average |error| 0.2678023
Relative |error| 0.38
Correlation coefficient 0.94
Attribute usage:
Conds Model
100% 54% host_copub_after_all
43% 100%
home_copub_after_all
57%
top_10_after_all
43%
home_copub_before_all
Time: 0.0 secs
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