predict.real_adaboost: predict method for real_adaboost objects

In souravc83/fastBoost: a fast implementation of adaboost.M1 and real adaboost

Description

predictions for model corresponding to real_adaboost algorithm makes predictions for an adaboost object on a new dataset using the real_adaboost algorithm, and also calculates the error if the target variable is avaiable in the 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.

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

Value

predictor predicted object with prediction probabilities

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)

souravc83/fastBoost documentation built on May 30, 2019, 6:34 a.m.