forward_selection: Select features based on feature selection

In UBC-MDS/feature-selection-r: Feature Selection

Description

Select features using forward selection algorithm. It starts as an empty model, and add the variable with the highest improvement in the accuracy of the model. The process is iteratively repeated and it stops when the remaining variables doesn't improve the accuracy of the model.

Usage

forward_selection(scorer, X, y, min_features = 1, max_features = 10)

Arguments

scorer	A custom user-supplied function that accepts X and y (as defined below) as input and returns the error of the datasets.
X	data frame of training features
y	data frame of training targets
min_features	double. number of minimum features to select
max_features	double. number of maximum features to select

Value

vector. The indexes of selected features

Examples

my_scorer <- function(data) {
  model <- lm(Y ~ ., data)
  return(mean(model$residuals^2))
}
data <- dplyr::select(tgp::friedman.1.data(), -Ytrue)
train_data <- data[1:(length(data)-1)]
test_data <- data[length(data)]
features <- featureselection::forward_selection(my_scorer, train_data, test_data, 3, 7)
# [1] 4 2 1 5

UBC-MDS/feature-selection-r documentation built on April 27, 2020, 7:21 p.m.