1 research outputs found
Beware of <i>R</i><sup>2</sup>: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models
The statistical metrics
used to characterize the external predictivity
of a model, i.e., how well it predicts the properties of an independent
test set, have proliferated over the past decade. This paper clarifies
some apparent confusion over the use of the coefficient of determination, <i>R</i><sup>2</sup>, as a measure of model fit and predictive
power in QSAR and QSPR modeling. <i>R</i><sup>2</sup> (or <i>r</i><sup>2</sup>) has been used in various contexts in the
literature in conjunction with training and test data for both ordinary
linear regression and regression through the origin as well as with
linear and nonlinear regression models. We analyze the widely adopted
model fit criteria suggested by Golbraikh and Tropsha (J. Mol. Graphics Modell. 2002, 20, 269β276) in a strict statistical manner. Shortcomings
in these criteria are identified, and a clearer and simpler alternative
method to characterize model predictivity is provided. The intent
is not to repeat the well-documented arguments for model validation
using test data but rather to guide the application of <i>R</i><sup>2</sup> as a model fit statistic. Examples are used to illustrate
both correct and incorrect uses of <i>R</i><sup>2</sup>.
Reporting the root-mean-square error or equivalent measures of dispersion,
which are typically of more practical importance than <i>R</i><sup>2</sup>, is also encouraged, and important challenges in addressing
the needs of different categories of users such as computational chemists,
experimental scientists, and regulatory decision support specialists
are outlined