Ensemble learning is a technique that involves combining multiple models to improve the accuracy and robustness of predictions.
Ensemble learning is commonly used in areas such as classification, regression, and anomaly detection.
It is particularly useful when dealing with complex or noisy data sets, where no single model can achieve high accuracy.
Ensemble learning is commonly used in areas such as classification, regression, and anomaly detection.
Ensemble learning has been applied in a wide range of fields, including finance, healthcare, and natural language processing.
Ensemble models can be difficult to interpret, as they combine the predictions of several models.
Ensemble models can be difficult to interpret, as they combine the predictions of several models.
Overall, ensemble learning can be a powerful technique for improving the performance of machine learning models, particularly in complex or noisy data sets.