# Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits

arXiv: https://arxiv.org/abs/2510.09465 · HTML: https://mlsanity.com/paper/2510.09465/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 10 Oct 2025.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Evaluating time series forecasting models: An empirical study on performance estimation methods](https://mlsanity.com/paper/1905.11744/) (2019)
- [A Fused Large Language Model for Predicting Startup Success](https://mlsanity.com/paper/2409.03668/) (2024)
- [Predictive Modeling and Explainable AI for Veterinary Safety Profiles, Residue Assessment, and Health Outcomes Using Real-World Data and Physicochemical Properties](https://mlsanity.com/paper/2510.01520/) (2025)
- [Citations or dollars? Early signals of a firm's research success](https://mlsanity.com/paper/2108.00200/) (2021)
- [Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds](https://mlsanity.com/paper/2404.16169/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Novel Multi-Timescale Stability-Preserving Hierarchical Reinforcement Learning Controller Framework for Adaptive Control in High-Dimensional Dynamical Systems](https://mlsanity.com/paper/2510.22420/) (2025)
- [Quantum Topological Graph Neural Networks for Detecting Complex Fraud Patterns](https://mlsanity.com/paper/2512.03696/) (2025)

## Abstract (arXiv)

This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated: next funding within 12 months, patent-stock growth within 24 months, and exit through an initial public offering (IPO) or acquisition within 36 months. Preprocessing is fit on a development window (2010-2019) and applied without change to later cohorts to avoid leakage. Class imbalance is addressed using inverse-prevalence weights and the Synthetic Minority Oversampling Technique for Nominal and Continuous features (SMOTE-NC). Logistic regression and tree ensembles, including Random Forest, XGBoost, LightGBM, and CatBoost, are compared using the area under the precision-recall curve (PR-AUC) and the area under the receiver operating characteristic curve (AUROC). Patent, funding, and exit predictions achieve AUROC values of 0.921, 0.817, and 0.872, providing transparent and reproducible rankings for innovation finance.