yh_RF


dataframe with last column for the output. The output must be a factor.


dfTr


dataframe for test data. Must have columns corresponding to the training columns except
the test output is not needed.


dfTe


Given training data and test examples, the RF predictions for the test data are produced and
the misclassification rate is returned.


 models

 classif 

Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.

yh_RF: Random Forest Prediction

Description

Usage

Arguments

Value

Examples