
Predict classification outcomes or probabilities from data
Source:R/predictFFTrees_function.R
predict.FFTrees.Rd
predict.FFTrees
predicts binary classification outcomes or their probabilities from newdata
for an FFTrees
object.
Usage
# S3 method for class 'FFTrees'
predict(
object = NULL,
newdata = NULL,
tree = 1,
type = "class",
sens.w = NULL,
method = "laplace",
data = NULL,
...
)
Arguments
- object
An
FFTrees
object created by theFFTrees
function.- newdata
dataframe. A data frame of test data.
- tree
integer. Which tree in the object should be used? By default,
tree = 1
is used.- type
string. What should be predicted? Can be
"class"
, which returns a vector of class predictions,"prob"
which returns a matrix of class probabilities, or"both"
which returns a matrix with both class and probability predictions.- sens.w, data
deprecated
- method
string. Method of calculating class probabilities. Either 'laplace', which applies the Laplace correction, or 'raw' which applies no correction.
- ...
Additional arguments passed on to
predict
.
See also
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.
Examples
# Create training and test data:
set.seed(100)
breastcancer <- breastcancer[sample(nrow(breastcancer)), ]
breast.train <- breastcancer[1:150, ]
breast.test <- breastcancer[151:303, ]
# Create an FFTrees object from the training data:
breast.fft <- FFTrees(
formula = diagnosis ~ .,
data = breast.train
)
#> ✔ Created an FFTrees object.
#> ✔ Ranked 9 cues (optimizing 'bacc').
#> ✔ Created 6 FFTs with 'ifan' algorithm (chasing 'bacc').
#> ✔ Defined 6 FFTs.
#> ✔ Applied 6 FFTs to 'train' data.
#> ✔ Ranked 6 FFTs by 'train' data.
#> ✔ Expressed 6 FFTs in words.
# Predict classification outcomes for test data:
breast.fft.pred <- predict(breast.fft,
newdata = breast.test
)
#> ✔ Applied 6 FFTs to 'test' data.
#> ✔ Generated predictions for tree 1.
# Predict class probabilities for test data:
breast.fft.pred <- predict(breast.fft,
newdata = breast.test,
type = "prob"
)
#> ✔ Applied 6 FFTs to 'test' data.
#> ✔ Generated predictions for tree 1.