What defines the boosting technique in ensemble learning?

The boosting technique in ensemble learning is fundamentally characterized by its approach of adding members sequentially to improve performance. In boosting, models are trained one after the other, where each new model focuses on correcting the errors made by the previous models. This sequential learning process allows for the enhancement of the overall model accuracy by emphasizing the instances that were misclassified earlier. As a result, boosting combines the outputs of multiple weak learners to create a strong learner, typically yielding improved predictive performance compared to individual models. This characteristic of sequentially addressing and minimizing the errors of prior models is what distinguishes boosting from other ensemble techniques.

The other options highlight different concepts that are not aligned with how boosting functions. Fitting multiple independent models on the same data describes bagging, whereas averaging predictions from different datasets pertains to techniques like stacking. Reducing data size through feature selection is an entirely separate process related to data preprocessing, not ensemble model training.