fftrees_ffttowords provides a verbal description
of tree definition (as defined in an FFTrees object).
Thus, fftrees_ffttowords translates an abstract FFT definition
into natural language output.
fftrees_ffttowords is the complement function to
fftrees_wordstofftrees, which parses a verbal description
of an FFT into the abstract tree definition of an FFTrees object.
The final sentence (or tree node) of the FFT's description
always predicts positive criterion values (i.e., TRUE instances) first,
before predicting negative criterion values (i.e., FALSE instances).
Note that this may require a reversal of exit directions,
if the final cue predicted FALSE instances.
Note that the cue directions and thresholds computed by FFTrees
always predict positive criterion values (i.e., TRUE or signal,
rather than FALSE or noise).
Using these thresholds for negative exits (i.e., for predicting instances of
FALSE or noise) usually requires a reversal (e.g., negating cue direction).
fftrees_ffttowords(x = NULL, mydata = "train", digits = 2)An FFTrees object created with FFTrees.
The type of data to which a tree is being applied (as character string "train" or "test").
Default: mydata = "train".
How many digits to round numeric values (as integer)?
A modified FFTrees object x with
x$trees$inwords containing a list of string vectors.
fftrees_wordstofftrees for converting a verbal description
of an FFT into an FFTrees object;
fftrees_create for creating FFTrees objects;
fftrees_grow_fan for creating FFTs by applying algorithms to data;
print.FFTrees for printing FFTs;
plot.FFTrees for plotting FFTs;
summary.FFTrees for summarizing FFTs;
FFTrees for creating FFTs from and applying them to data.
heart.fft <- FFTrees(diagnosis ~ .,
data = heartdisease,
decision.labels = c("Healthy", "Disease")
)
#> ✔ Created an FFTrees object.
#> Ranking 13 cues: ■■■■■■ 15% | ETA: 1s
#> Ranking 13 cues: ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 100% | ETA: 0s
#>
#> ✔ Ranked 13 cues (optimizing 'bacc').
#> ✔ Created 7 FFTs with 'ifan' algorithm (chasing 'bacc').
#> ✔ Defined 7 FFTs.
#> ✔ Applied 7 FFTs to 'train' data.
#> ✔ Ranked 7 FFTs by 'train' data.
#> ✔ Expressed 7 FFTs in words.
inwords(heart.fft)
#> [1] "If thal = {rd,fd}, decide Disease."
#> [2] "If cp != {a}, decide Healthy."
#> [3] "If ca > 0, decide Disease, otherwise, decide Healthy."