Ensemble learning is a machine learning technique that combines the predictions from two or more models to produce a more accurate or robust prediction than any individual model. The core idea is that by aggregating the "votes" or predictions of multiple models, the weaknesses of individual models can be mitigated, leading to improved overall performance and generalization. Common ensemble methods include bagging (e.g., Random Forest), boosting (e.g., Gradient Boosting), and stacking.

B. Data partitioning: This is the process of splitting a dataset into subsets (e.g., train, test, validate) for model development and evaluation, not combining models.

C. Feature scaling: This is a data preprocessing technique used to normalize the range of features, which aids in model training but does not involve combining models.

D. Model deployment: This is the operational process of making a trained model available for use, which is distinct from the training technique of combining models.

1. Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In Multiple Classifier Systems, First International Workshop, MCS 2000 Cagliari, Italy, June 21–23, 2000 Proceedings (pp. 1-15). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-45014-91 (Section 1, "Introduction").

2. Microsoft. (2023). Automated machine learning (AutoML). Azure Machine Learning Documentation. Retrieved from https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml. (The documentation notes that AutoML uses "voting and stacking ensemble models" as a final iteration to improve results).

3. Ng, A. (n.d.). Machine Learning. Stanford University Courseware (CS229). Retrieved from http://cs229.stanford.edu/notes2019fall/cs229-notes-ensemble.pdf (Section 1, "Ensemble learning").