  Measuring the value of individual samples is critical for many data-driven
tasks, e.g., the training of a deep learning model. Recent literature witnesses
the substantial efforts in developing data valuation methods. The primary data
valuation methodology is based on the Shapley value from game theory, and
various methods are proposed along this path. {Even though Shapley value-based
valuation has solid theoretical basis, it is entirely an experiment-based
approach and no valuation model has been constructed so far.} In addition,
current data valuation methods ignore the interpretability of the output
values, despite an interptable data valuation method is of great helpful for
applications such as data pricing. This study aims to answer an important
question: is data valuation learnable and interpretable? A learned valuation
model have several desirable merits such as fixed number of parameters and
knowledge reusability. An intrepretable data valuation model can explain why a
sample is valuable or invaluable. To this end, two new data value modeling
frameworks are proposed, in which a multi-layer perception~(MLP) and a new
regression tree are utilized as specific base models for model training and
interpretability, respectively. Extensive experiments are conducted on
benchmark datasets. {The experimental results provide a positive answer for the
question.} Our study opens up a new technical path for the assessing of data
values. Large data valuation models can be built across many different
data-driven tasks, which can promote the widespread application of data
valuation.
