  Quantifying the value of data within a machine learning workflow can play a
pivotal role in making more strategic decisions in machine learning
initiatives. The existing Shapley value based frameworks for data valuation in
machine learning are computationally expensive as they require considerable
amount of repeated training of the model to obtain the Shapley value. In this
paper, we introduce an efficient data valuation framework EcoVal, to estimate
the value of data for machine learning models in a fast and practical manner.
Instead of directly working with individual data sample, we determine the value
of a cluster of similar data points. This value is further propagated amongst
all the member cluster points. We show that the overall value of the data can
be determined by estimating the intrinsic and extrinsic value of each data.
This is enabled by formulating the performance of a model as a
\textit{production function}, a concept which is popularly used to estimate the
amount of output based on factors like labor and capital in a traditional free
economic market. We provide a formal proof of our valuation technique and
elucidate the principles and mechanisms that enable its accelerated
performance. We demonstrate the real-world applicability of our method by
showcasing its effectiveness for both in-distribution and out-of-sample data.
This work addresses one of the core challenges of efficient data valuation at
scale in machine learning models. The code is available at
\underline{https://github.com/respai-lab/ecoval}.
