Quality evaluation of film and television talent cultivation based on Random Forest Groves Algorithm

Evaluating film and television talent development quality plays a central role in improving learning approaches and correlating training outcomes with market requirements. Traditional assessment methods are subjective or rooted in static parameters, not taking into account the complex, multifaceted nature of talent development. Present systems primarily resort to simple statistical analysis or manual models of evaluation, resulting in incongruous findings and inadequate scalability. As a response to challenges of this nature, this study proposes the implementation of data-driven assessment based on the Random Forest Groves (RFG) algorithm to give an unbiased measure of the performance of talent development programs within film and television education. The model stacks the different parameters fof random Forest to understand the variety of indicators such as skill growth, creative performance, cooperative competence, and sectorial applicability. The RFG’s nature of ensemble learning allows it to be able to handle nonlinear relationships, ranking of feature importance, and reducing the threats of overfitting. The novel model compares to more conventional methods such as decision trees or linear regression and is more predictive and robust. The results demonstrate that our Random Forest model performs extremely well in evaluation accuracy with accuracy at 97%, precision at 97.01%, recall at 97%, specificity at 97.1%, and F1-score at 97%. These findings demonstrate the potential of intelligent assessment systems to support data-driven educational policy and enhance the training of television and film professionals.