What is the term for a machine learning technique that involves training models on multiple subsets of the data and combining their predictions?

The correct answer is D. Ensemble learning.

Ensemble learning is a machine learning technique that involves training multiple models on the same data set and then combining their predictions to produce a more accurate result. This can be done in a number of ways, such as by averaging the predictions of the individual models or by using a voting scheme.

Ensemble learning can be more accurate than any of the individual models in the ensemble, and it can also be more robust to noise in the data. This makes it a valuable tool for a variety of machine learning tasks.

Here is a brief explanation of each of the options:

• Feature engineering is the process of identifying and selecting features from a data set that are relevant to the task at hand. This can be a complex and time-consuming process, but it can be essential for achieving good results in machine learning.
• Regularization is a technique that is used to prevent overfitting in machine learning models. This can be done by adding a penalty term to the loss function that discourages the model from becoming too complex.
• Clustering is a technique that is used to group data points together based on their similarity. This can be used for a variety of tasks, such as data visualization and anomaly detection.

I hope this helps! Let me know if you have any other questions.