Predict classification outcomes or probabilities from data

predict.FFTrees predicts binary classification outcomes or their probabilities from newdata for an FFTrees object.

# S3 method for FFTrees
predict(
  object = NULL,
  newdata = NULL,
  tree = 1,
  type = "class",
  sens.w = NULL,
  method = "laplace",
  data = NULL,
  ...
)

Arguments

object

An FFTrees object created by the FFTrees 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.

Value

Either a logical vector of predictions, or a matrix of class probabilities.

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.
#>   Ranking 9 cues:  ■■■■■■■■                          22% | ETA:  0s
#>   Ranking 9 cues:  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s
#> 
#> ✔ 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.