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Ensemble methods combine the predictions of multiple models to achieve better performance than what a single model can provide. It helps in utilizing the strengths of different algorithms and models while also minimizing their weaknesses. This will in turn help in providing more accurate and actionable predictions. How Ensemble Methods Improve Prediction Accuracy and Robustness: Instead of training one model on an entire dataset, if we train multiple models on different subsets of the same data, we can average their predictions and in turn reduce the variance across the data set. This process is known as bagging and helps reduce the variance caused by data sampling. Ensemble methods help in identifying the patterns which are very subtle, are outliers or anomalies in the data set which individual models may miss out. This helps in improving the performance of models on difficult to predict data points. This in turn reduces missing out on information and helps overcome bias. This is known as boosting. Ensemble methods help in bringing together the strengths of every model being used while also eliminating or reducing the weak points. This can be done through stacking which trains the model on the predictions of multiple different models. Advantages of Ensemble Methods in Business Decision-Making: Ensembles help in providing a more accurate prediction from the datasets. This helps us in making better and data-driven informed decisions. The sensitivity to various outliers and noise in a dataset is reduced when we use ensemble methods. This makes the final results more reliable. Ensemble methods help in better generalization to new data. This helps in reducing the risk of model failure when unseen situations come up. They are not limited to just one business problem and can be applied to a wide range of business problems, including but not limited to customer churn prediction, fraud detection, risk assessment, and market forecasting. Limitations of Ensemble Methods: Due to utilization of various models which can have varied levels of complexities, ensemble methods may tend to become more complex to implement and interpret than single models. When we utilize large datasets, it may become tedious as well as expensive to train and deploy these ensemble models.. Due to combining multiple models and their predictions, it may become difficult to get to the root of a decision made by ensemble models. Example: Risk & Fraud Prediction A credit company wants to reduce credit risk and fraudulent practices. Instead of using one single model, the data set can be entered in different models related to credit, fraud and risk models where we can get the generalized data with lesser variance, more robust solutions and more accurate predictions.