Manually specifying FFTs

my.tree

The first method is to use the my.tree argument, where my.tree is a sentence describing a (single) FFT. When this argument is specified in FFTrees(), the function (specifically wordstoFFT() will try to extract the specified FFT from the argument.

For example, let’s look at the columns sex, age and thal in the heartdisease data:

head(heartdisease[c("sex", "age", "thal")])

## # A tibble: 6 × 3
##     sex   age thal  
##   <dbl> <dbl> <chr> 
## 1     1    63 fd    
## 2     1    67 normal
## 3     1    67 rd    
## 4     1    37 normal
## 5     0    41 normal
## 6     1    56 normal

Here’s how we could specify an FFT using these cues as a sentence:

my.tree = "If sex = 1, predict True.
           If age < 45, predict False. 
           If thal = {fd, normal}, predict True. Otherwise, predict False"

Here are some notes on specifying trees manually:

• Each node must start with the word “If” and should in the form: If <CUE> <DIRECTION> <THRESHOLD>, predict <EXIT>.
• Numeric thresholds shold be specified directly (without brackets).
• Factor thresholds must be specified within braces like sex = {male}. For factors with sets of values, values within a threshold should be separated by commas like eyecolor = {blue,brown}
• Standard logical comparisons =, !=, <, >= (etc.) are valid. For numeric cues, only use >, >=, <, <=. For factors, only use = and !=.
• Positive exits are indicated by True, while negative exits are specified by False. The final node will be forced to have a bidirectional exit. The text Otherwise, predict EXIT I’ve included in the example above is actually not necessary.

Now, let’s pass the my.tree argument to FFTrees() to force apply our FFT to the heartdisease data:

# Pass a verbally defined FFT to FFTrees with the my.tree argument
my.heart.fft <- FFTrees(diagnosis ~.,
                        data = heartdisease,
                        my.tree = "If sex = 1, predict True.
                                   If age < 45, predict False. 
                                   If thal = {fd, normal}, predict True. 
                                   Otherwise, predict False")

Let’s see how well our FFT did:

# Plot 
plot(my.heart.fft)

As you can see, this FFT is pretty terrible – it has a high sensitivity, but a terrible specificity.

Let’s see if we can come up with a better one using the cues thal, cp, and ca

# Specify an FFt verbally with the my.tree argument
my.heart.fft <- FFTrees(diagnosis ~.,
                        data = heartdisease,
                        my.tree = "If thal = {rd,fd}, predict True.
                                   If cp != {a}, predict False. 
                                   If ca > 1, predict True. 
                                   Otherwise, predict False")

# Plot 
plot(my.heart.fft)

This one looks much better!