The CrossR package (short for Cross-validation in R) is a set of functions for implementing cross-validation inside the R environment.
- Nazli Ozum Kafaee / @nazliozum
- Daniel Raff / @raffrica
- Shun Chi / @ShunChi100
Cross-validation is an important technique used in model selection and hyper-parameter optimization. Scores from cross-validation are a good estimation of test score of a predictive model in test data or new data as long as the IID assumption approximately holds in data. This package aims to provide a standardized pipeline for performing cross-validation for different modeling functions in R. In addition, summary statistics of the cross-validation results are provided for users.
Three main functions in CrossR:
-
train_test_split(): This function partitions data intok-fold and returns the partitioned objects. A random shuffling option is provided. -
cross_validation(): This function performsk-fold cross validation using the partitioned data and a selected model. It returns the scores of each validation. Additional methods for corss validation will be implemented (such as "Leave-One-Out" if time allows). -
summary_cv(): This function outputs summary statistics(mean, standard deviation, mode, median) of cross-validation scores.
Additionally, we've built a helper function that generates data for the above functions:
gen_data(): This function generates a list of X and y data that can be passed in totrain_test_split()orcross_validation()
It can be used as follows:
data <- gen_data(100)
X <- data.frame(data[[1]])
y_vec <- data[[2]]
y <- data.frame(y = y_vec)
To install the package:
devtools::install_github("UBC-MDS/CrossR")
Given X (explanatory variable) and y (response variable) as dataframes/atomic vectors, one can split the data
library(CrossR)
split_data <- train_test_split(X, y, test_size = 0.25, random_state = 0, shuffle = TRUE)
# to assign split data into individual variables
X_train = split_data[[1]]
X_test = split_data[[2]]
y_train = split_data[[3]]
y_test = split_data[[4]]
To do cross-validation on X, y using the linear regression lm() model:
scores <- cross_validation(split_data[['X_train']], split_data[['y_train']])
To see the summary of scores:
summary_cv(scores)
Cross-validation can be implemented with the caret package in R. caret contains the function createDataPartition() to split the data and train_Control() to apply cross-validation with different methods depending on the method argument. We have observed that caret functions have some features that make the cross-validation process cumbersome. createDataPartition() splits the indices of the data which could be used later on to actually split the data into training and test data. This will be applied with one step using split_data() in CrossR.
This is an open source project. So feedback, suggestions and contributions are very welcome. For feedback and suggestions, please open an issue in this repo. If you are willing to contribute this package, please refer Contributing guidelines for details.
