Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach

Predicting innovation outcomes at the firm level continues to be an important but challenging goal for researchers and practitioners alike. In this study, multiple machine learning models, encompassing both ensemble-based and single-model approaches, were applied to data from the Community Innovatio...

詳細記述

書誌詳細

出版年:	Applied Sciences
主要な著者:	Marko Martinović, Kristian Dokic, Dalibor Pudić
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2025-03-01
主題:	innovation prediction machine learning ensemble methods cross-validation classification performance computational efficiency
オンライン･アクセス:	https://www.mdpi.com/2076-3417/15/7/3636