chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models
It offers a practical tool for cheminformatics researchers to compare machine learning models efficiently, though it is incremental as it packages existing methods into a user-friendly interface.
The paper introduces chemmodlab, an R package that streamlines fitting and assessing machine learning models, particularly for cheminformatics, by providing tools like accumulation curves and a multiple comparisons similarity plot to easily compare model performance with statistical significance.
The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of new models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities including a plotting function that constructs accumulation curves and a function that computes many performance measures. The most novel feature of chemmodlab is the ease with which statistically significant performance differences for many machine learning models is presented by means of the multiple comparisons similarity plot. Differences are assessed using repeated k-fold cross validation where blocking increases precision and multiplicity adjustments are applied.