"UIC GII ML SMOTE"

"University-industry collaborations (UIC) drive innovation and technology transfer. However, predicting the performance of these partnerships remains a persistent methodological challenge. Traditional statistical techniques are insufficient in capturing non-linear relationships in datasets. While machine learning (ML) models are progressively applied in UIC research, their implementation in Africa is limited. This paper employs ML to classify UIC performance across 32 African countries into three categories (weak, moderate and strong), using panel data from the Global Innovation Index (2013\u20132022). Three key indicators are analyzed: (1) institutional factors, (2) infrastructure, and (3) human capital & research factors. Additionally, the study identifies information and communication technology, research and development, business environment, regulatory environment and tertiary education as critical UIC success factors in Africa. These insights provide policymakers, universities, and industry stakeholders with actionable strategies to strengthen collaborative outcomes and enhance Africa\u2019s global competitiveness. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied at varying ratios (100%\u2013600%). A comparative evaluation of Random Forest (RF), K-Nearest Neighbors (KNN), Neural Networks (NN), and J48 models demonstrates that the RF model enhanced with SMOTE at 500% (RF-SMOTE500) achieves the highest classification accuracy (96.3%), outperforming other SMOTE-augmented models (KNN-SMOTE600: 93.6%, J48-SMOTE500: 90.2%, NN: 80.4%) and baseline models without SMOTE (RF: 88.7%, NN: 86.1%, KNN: 84.7%, J48: 83.1%). The findings indicate that SMOTE significantly improves RF, J48 and KNN models, highlighting the importance of data balancing. However, the decline in NN performance after SMOTE, suggests that SMOTE application requires caution, particularly with neural networks."