predict.real_adaboost: predict method for real_adaboost objects

In fastAdaboost: a Fast Implementation of Adaboost

Description

predictions for model corresponding to real_adaboost algorithm

Usage

## S3 method for class 'real_adaboost'
predict(object, newdata, ...)

Arguments

object	an object of class real_adaboost
newdata	dataframe on which we are looking to predict
...	arguments passed to predict.default

Details

makes predictions for an adaboost object on a new dataset using the real_adaboost algorithm. 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.

Value

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 NA

See Also

real_adaboost

Examples

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_real_adaboost <- real_adaboost(Y~X, fakedata, 10)
pred <- predict(test_real_adaboost,newdata=fakedata)
print(pred$error)
print( table(pred$class,fakedata$Y) )

fastAdaboost documentation built on May 2, 2019, 3:33 p.m.