Description Usage Arguments Value Author(s) Examples
An iterative semisupervised learning approach using pivot features. Uses a random forest classifier to train and predict probabilities.
1  iter_pivot(source, target, x_pivot, x_nonpivot, rho = 3)

source 
A data frame containing observations and features for the source domains. Must contain a column named "Domain" which specifies the domain each observation belongs to and "Class" which gives the response. 
target 
A data frame containing unlabelled observations and features for the target domain. 
x_pivot 
A vector of strings. Gives the column names of features with common distribution between source and target domains. 
x_nonpivot 
A vector of strings. Gives the column names of features that have a shifted distribution between the target and source domains. 
rho 
Number of observations to be added to labelled target training set (actual number added is 4*rho). 
iter_pivot
returns an object of class "iter_pivot".
The function plot
produces a plot showing predictions for the most recent
iteration in the two feature spaces.
An object of class "iter_pivot" is a list containing the following components:
final_pred
A data frame containing final predictions.
all_preds
A data frame containing predictions for each iteration and
model.
Cameron Roach
1 2 3 4 5 6  source < sim_pivot_data[sim_pivot_data$Domain == "Source",]
target < sim_pivot_data[sim_pivot_data$Domain == "Target",]
predictions < iter_pivot(source = source, target = target,
x_pivot = "Feature1", x_nonpivot = "Feature2",
rho = 3)
#TODO: FIX plot(predictions, aes(x = Feature1, y = Feature2))

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