predict method for adaboost objects
predictions for model corresponding to adaboost.m1 algorithm
## S3 method for class 'adaboost' predict(object, newdata, ...)
object |
an object of class adaboost |
newdata |
dataframe on which we are looking to predict |
... |
arguments passed to predict.default |
makes predictions for an adaboost object on a new dataset.
The target variable is not required
for the prediction to work.
However, the user must ensure that the test data has the same
columns which were used as inputs to fit the original model.
The error component of the prediction object(as in
pred$error) can be used to get the error of the
test set if the test data is labeled.
predicted object, which is a list with the following components
formula |
the formula used. |
votes |
total weighted votes achieved by each class |
class |
the class predicted by the classifier |
prob |
a matrix with predicted probability of each class for each observation |
error |
The error on the test data if labeled, otherwise |
fakedata <- data.frame( X=c(rnorm(100,0,1),rnorm(100,1,1)), Y=c(rep(0,100),rep(1,100) ) ) fakedata$Y <- factor(fakedata$Y) test_adaboost <- adaboost(Y~X, fakedata, 10) pred <- predict( test_adaboost,newdata=fakedata) print(pred$error) print( table(pred$class,fakedata$Y) )
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