IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

An ensemble method in machine learning refers to a technique that combines multiple learning algorithms to improve the overall predictive performance. This approach leverages the strengths of different models to create a more robust and accurate prediction than any single model could achieve on its own. By aggregating the outputs of various models, ensemble methods can reduce the likelihood of overfitting and increase generalization to new data.

Common ensemble techniques include bagging, boosting, and stacking. For instance, in bagging, multiple versions of a model are trained on different subsets of the data, and their predictions are averaged (or voted on) to mitigate variance. Boosting, on the other hand, aims to improve the performance of weak learners by focusing on the mistakes made by previous models in the sequence.

The other options do not accurately describe ensemble methods. Splitting data into groups pertains to data preprocessing techniques rather than the integration of models. Data transformation processes relate to changing the format or structure of the data without necessarily combining models. Clustering techniques focus on grouping data points based on similarities but do not involve the collaboration of multiple learning algorithms for prediction.