# Which products activate a product? An explainable machine learning approach

arXiv: https://arxiv.org/abs/2212.03094 · HTML: https://mlsanity.com/paper/2212.03094/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 60 references that are in the map.
- First posted on arXiv 5 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Product Progression: a machine learning approach to forecasting industrial upgrading](https://mlsanity.com/paper/2105.15018/) (2021)
- [Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production](https://mlsanity.com/paper/1707.05146/) (2017)
- [Machine learning to assess relatedness: the advantage of using firm-level data](https://mlsanity.com/paper/2202.00458/) (2022)

## Abstract (arXiv)

Tree-based machine learning algorithms provide the most precise assessment of the feasibility for a country to export a target product given its export basket. However, the high number of parameters involved prevents a straightforward interpretation of the results and, in turn, the explainability of policy indications. In this paper, we propose a procedure to statistically validate the importance of the products used in the feasibility assessment. In this way, we are able to identify which products, called explainers, significantly increase the probability to export a target product in the near future. The explainers naturally identify a low dimensional representation, the Feature Importance Product Space, that enhances the interpretability of the recommendations and provides out-of-sample forecasts of the export baskets of countries. Interestingly, we detect a positive correlation between the complexity of a product and the complexity of its explainers.