## 1 Introduction  
Predicting and fostering innovation is pivotal to maintaining a competitive advantage in today's rapidly evolving business landscape. Innovation outcomes--ranging from the successful development of new products to the implementation of improved processes--have been shown to significantly influence a company's long-term growth, market share, and overall sustainability. Consequently, identifying data-driven, reliable ways to forecast these outcomes has become a priority for both researchers and practitioners in the fields of entrepreneurship, strategy, and policy. In particular, machine learning (ML) has grown increasingly popular for extracting actionable insights from large and complex datasets. Yet, the broad range of available ML methods, each with distinct strengths and weaknesses, raises a crucial question: which approaches offer the most reliable and accurate predictions?Addressing this gap is not only important for academic research but also for practitioners aiming to allocate resources effectively and guide strategic decision-making around innovation [1].  
Over the last two decades, advances in computational power and algorithms have fostered the emergence of sophisticated ML techniques that surpass traditional statistical methods in various prediction tasks [2; 3; 4; 5; 6; 7; 8]. Among these, ensemble methods stand out for their ability to combine the predictions of multiple learners, thereby mitigating overfitting and enhancing generalization. Bagging and boosting approaches such as Random Forest, XGBoost, CatBoost, and LightGBM have repeatedly demonstrated robust predictive capabilities across diverse domains [2; 3; 4; 5; 7]. Notwithstanding these strengths, individual models--including classical Linear and Logistic Regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANNs)--train notable advantages, such as simplicity, interpretability, and in some cases, superior performance on smaller datasets. For instance, Logistic Regression is widely recognized for its computational efficiency and suitability for binary classification tasks [9]. Likewise, SVM often excels in high-dimensional settings and smaller samples [10]. ANNs, while powerful universal approximators [11], can be prone to overfitting when data are limited, although advancements in regularization techniques partly address this challenge [12; 13; 14].  
Within the sphere of innovation studies, the Community Innovation Survey (CIS) [15] is among the most comprehensive data collection initiatives tracking firm-level innovation activities and outcomes. By analyzing a sample of the CIS2014 [16] dataset from Croatian companies, this study seeks to illuminate which ML models best predict whether a firm will achieve successful innovation outcomes--thereby offering insight not only for Croatia's evolving economy but also as a transferable framework for other contexts and datasets. However, a range of divergent hypotheses exists regarding which techniques--ensemble or individual--will emerge as the most accurate and efficient.  
For example, some researchers argue that ensemble methods almost invariably outperform single learners in complex prediction tasks [2; 3; 7; 17] while others highlight scenarios where simpler algorithms or certain specialized approaches prevail [9; 10; 13]. Additionally, new boosting algorithms such as CatBoost are specifically designed to handle categorical data more effectively [7], raising the possibility that they may surpass both traditional boosting and non-boosting methods.