---
title: Software estimation with statistical models
url: https://www.ml-quant.com/papers/repec/gam-jmathe-v-12-y-2024-i-7-p-1058-d-1368511/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1058-:d:1368511
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F12%2F7%2F1058%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A12%3Ay%3A2024%3Ai%3A7%3Ap%3A1058-%3Ad%3A1368511
featured: 2024-04-24
citations: unknown
topic: ML & AI Methods
---


# Software estimation with statistical models

Machine learning techniques, particularly Random Forest, can accurately predict software development time and effort, reducing the risk of miscalculations.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.mdpi.com%2F2227-7390%2F12%2F7%2F1058%2Fpdf%3Bh%3Drepec%3Agam%3Ajmathe%3Av%3A12%3Ay%3A2024%3Ai%3A7%3Ap%3A1058-%3Ad%3A1368511
- Identifier: RePEc:gam:jmathe:v:12:y:2024:i:7:p:1058-:d:1368511
- Released: 2024-04-24
- First featured: Quant Letter No. 46 (2024-04-24): https://www.ml-quant.com/issues/2024-04-24/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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