Interpreting Startup Partnership Potential: A Case Study of Digital Startups Using ID3 Decision Trees
Bibliographic record
Abstract
This study proposes an interpretable, yet effective data-driven framework for predicting corporate–startup partner selection, focusing on extracting understandable business rules to guide managerial decisions. Using publicly available startup information and a company’s historical collaboration records, a decision tree model was developed to identify the key factors influencing selection decisions and classify potential startups. Based on a case study of a technology outsourcing firm, our decision tree model, with a prediction accuracy of 92%, revealed that the company favored startups with strong technological expertise – particularly in Data Analytics, Internet of Things, and Software Development – combined with stable funding and clear, explicit communication for collaboration. The model also captured a distinct interaction, where earlier-stage startups' requesting mentoring needs were also considered by the company, but for a different purpose – mentorship. These findings demonstrate how interpretable models can reveal meaningful insights related to a company’s strategic goals, supporting more confident partner selection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".