emil: Introduction to the emil package

Description

The emil package implements a framework for working with predictive modeling problems without information leakage. For an overview of its functionality please read the original publication included as the package's vignette (to be added).

Arguments

Central topics and functions

Setting up modeling problems

resample: Functions for generating and resampling schemes and information on how to implement custom resampling methods.
pre_process: Data pre-processing functions.
modeling_procedure: Manages algorithms used for fitting models, making predictions, and extracting feature importance scores.
error_fun: Performance estimation functions used to tune parameters and evaluate performance of modeling procedures.

Solving modeling problems

fit: Fit a model (according to a procedure).
tune: Tune parameters of a procedure.
predict: Use a fitted model to predict the response of observations.
evaluate: Evaluate the performance of a procedure using resampling.
learning_curve: Learning curve analysis.

Managing the results of modeling problems

get_prediction: Extract predictions from resampled modeling results.
get_tuning: Extract feature importance scores of a fitted model or resampled modeling results.
get_importance: Extract feature importance scores of a fitted model or resampled modeling results.
subtree: Extracts results from the output of evaluate. It is essentially a recursive version of lapply and sapply.
select: Interface between emil and the dplyr package for data manipulation. Can be used to subset modeling results, reorganize or summarize to help interpretation or prepare for plotting.

Methods included in the package

Resampling methods

See resample for information on usage and implementation of custom methods.

resample_holdout: Repeated holdout.
resample_crossvalidation: Cross validation.

Data pre-processing methods

See pre_process for information on usage and implementation of custom methods. The imputation functions can also be used outside of the resampling scheme, see impute.

pre_split: Only split, no transformation.
pre_center: Center data to have mean 0 of each feature.
pre_scale: Center and scale data to have mean 0 and standard deviation 1.
pre_impute_median: Impute missing values with feature medians.
pre_impute_knn: Impute missing values with k-NN, see pre_impute_knn for details on how to set parameters.

Modeling methods

The following modeling methods are included in the emil package. For a complete list of available methods in both the emil package and other loaded packages, please use list_method. See modeling_procedure for information on usage and extension for information on implementation of custom methods.

cforest: Conditional inference forest.
coxph: Cox proportional hazards model.
glmnet: Elastic net.
lasso: LASSO.
lda: Linear discriminant.
lm: Linear model.
pamr: Nearest shrunken centroids.
qda: Quadratic discriminant.
randomForest: Random forest.
ridge_regression: Ridge regression.
rpart: Decision trees.

It is also possible to incorporate any method from the ‘caret’ package by using the function fit_caret.

To search for emil compatible methods in all attached packages use the list_method function.

Performance estimation methods

See error_fun for information on usage and implementation of custom methods. Since the framework is designed to minimize the error when tuning parameters, some measures are negated, e.g. neg_auc.

For classification problems:

error_rate: Fraction of predictions that were incorrect.
weighted_error_rate: See its own documentation.
neg_auc: Negative area under ROC curve. To plot the ROC curves see roc_curve.
neg_gmpa: Negative geometric mean of class-specific prediction accuracy. Good for problems with imbalanced class sizes.

For regression problems:

mse: Mean square error.
rmse: Root mean square error.

For survival analysis problem:

neg_harrell_c: Negative Harrell's concordance index.

Plotting

Plotting is not the one of the main aims of the package and the methods that do exist mainly serves as examples for how to write your own. These exists for: