Predicts rule_nbr applicable (as per the order in rulelist)
for a row_nbr (per key) in new_data
# S3 method for rulelist
predict(object, new_data, multiple = FALSE, ...)A rulelist
(dataframe)
(flag, default: FALSE) Whether to output all rule numbers applicable for a row. If FALSE, the first satisfying rule is provided.
unused
A dataframe. See Details.
If a row_nbr is covered more than one rule_nbr per 'keys', then
rule_nbr appearing earlier (as in row order of the rulelist) takes
precedence.
When multiple is FALSE(default), output is a dataframe with three
or more columns: row_number (int), columns corresponding to 'keys',
rule_nbr (int).
When multiple is TRUE, output is a dataframe with three
or more columns: row_number (int), columns corresponding to 'keys',
rule_nbr (list column of integers).
If a row number and 'keys' combination is not covered by any rule, then
rule_nbr column has missing value.
model_c5 = C50::C5.0(species ~.,
data = palmerpenguins::penguins,
trials = 5,
rules = TRUE
)
tidy_c5 = tidy(model_c5)
tidy_c5
#> ---- Rulelist --------------------------------
#> ▶ Keys: trial_nbr
#> ▶ Number of distinct keys: 5
#> ▶ Number of rules: 26
#> ▶ Model type: C5
#> ▶ Estimation type: classification
#> ▶ Is validation data set: FALSE
#>
#>
#> rule_nbr trial_nbr LHS RHS support confidence lift
#> <int> <int> <chr> <fct> <int> <dbl> <dbl>
#> 1 1 1 ( island == 'Biscoe' ) & (… Gent… 122 0.992 2.8
#> 2 2 1 ( bill_length_mm <= 43.3 )… Adel… 68 0.986 2.2
#> 3 3 1 ( island == 'Dream' ) & ( … Chin… 48 0.98 5
#> 4 4 1 ( bill_length_mm > 42.3 ) … Chin… 34 0.944 4.8
#> 5 5 1 ( flipper_length_mm <= 206… Adel… 213 0.698 1.6
#> 6 1 2 ( bill_length_mm <= 40.8 ) Adel… 86 0.989 3
#> 7 2 2 ( island == 'Torgersen' ) Adel… 39 0.976 2.9
#> 8 3 2 ( island == 'Biscoe' ) & (… Adel… 32 0.971 2.9
#> 9 4 2 ( island == 'Dream' ) & ( … Chin… 87 0.910 3.9
#> 10 5 2 ( island == 'Biscoe' ) Gent… 183 0.816 1.9
#> # ℹ 16 more rows
#> ----------------------------------------------
output_1 = predict(tidy_c5, palmerpenguins::penguins)
output_1 # different rules per 'keys' (`trial_nbr` here)
output_2 = predict(tidy_c5, palmerpenguins::penguins, multiple = TRUE)
output_2 # `rule_nbr` is a list-column of integer vectors