  The selection of the target variable is important while learning parameters
of the classical car following models like GIPPS, IDM, etc. There is a vast
body of literature on which target variable is optimal for classical car
following models, but there is no study that empirically evaluates the
selection of optimal target variables for black-box models, such as LSTM, etc.
The black-box models, like LSTM and Gaussian Process (GP) are increasingly
being used to model car following behavior without wise selection of target
variables. The current work tests different target variables, like
acceleration, velocity, and headway, for three black-box models, i.e., GP,
LSTM, and Kernel Ridge Regression. These models have different objective
functions and work in different vector spaces, e.g., GP works in function
space, and LSTM works in parameter space. The experiments show that the optimal
target variable recommendations for black-box models differ from classical car
following models depending on the objective function and the vector space. It
is worth mentioning that models and datasets used during evaluation are diverse
in nature: the datasets contained both automated and human-driven vehicle
trajectories; the black-box models belong to both parametric and non-parametric
classes of models. This diversity is important during the analysis of variance,
wherein we try to find the interaction between datasets, models, and target
variables. It is shown that the models and target variables interact and
recommended target variables don't depend on the dataset under consideration.
