Ensemble modeling is a technique in which multiple predictive models are combined to improve the overall performance and accuracy of the predictions. This approach leverages the diversity of different models to create a more robust and reliable final prediction. Ensemble modeling can be applied in various fields, such as machine learning, statistics, and data mining, and is commonly used in tasks such as classification, regression, and clustering. Some popular ensemble modeling techniques include bagging, boosting, and stacking. These techniques have been shown to consistently outperform individual models and are widely used in practice to improve the quality of predictions.