I'm new to class imbalance and applying class imbalance technique 'RusBoost' on my dataset. I'm using ebmc package from R. I'm having difficulties to get its arguements values, as per the documentation:
data("iris") iris <- iris[1:70, ] iris$Species <- factor(iris$Species, levels = c("setosa", "versicolor"), labels = c("0", "1")) model2 <- rus(Species ~ ., data = iris, size = 20, alg = "rf", ir = 1, rf.ntree = 100)
Documentation gives the following details about arguments:
A formula specify predictors and target variable. Target variable should be a factor of 0 and 1. Predictors can be either numerical and categorical.
A data frame used for training the model, i.e. training set.
Ensemble size, i.e. number of weak learners in the ensemble model.
The learning algorithm used to train weak learners in the ensemble model. cart, c50, rf, nb, and svm are available. Please see Details for more information.
Imbalance ratio. Specifying how many times the under-sampled majority instances are over minority instances. Interger is not required and so such as ir = 1.5 is allowed.
Number of decision trees in each forest of the ensemble model when using rf (Random Forest) as base learner. Integer is required.
This is the link for a documentation for your reference: https://cran.r-project.org/web/packages/ebmc/ebmc.pdf
I couldn't able to figure out size and ir parameter values. How could I? I have a binary classification problem, which predicting: "Yes" , "No". There are 34% "Yes" labeled data and 66% "No" labeled data in my dataset(total 15,000 records).
Thanks in advance.