Final answer:
The modeling technique that uses many small trees is c. Random Forests. Random Forests create a 'forest' of many smaller, diverse decision trees to improve prediction accuracy and stability.
Step-by-step explanation:
Among the modeling techniques listed, the one that tends to use many small trees is c. Random Forests. Random Forests is an ensemble method in machine learning that operates by constructing a multitude of decision trees at training time and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees. The 'forest' in Random Forests is made up of many decision trees that are used to produce a more accurate and stable prediction. In contrast, Gradient Boosting also uses trees but in a sequential manner where each tree is built to correct the errors of the previous one. Therefore, while Gradient Boosting does use multiple trees, they are often larger and more complex than the trees used in Random Forests, focusing on reducing the error margin step by step.
Random Forests model adds robustness to the model by using a diverse set of trees to ensure that the individual errors of some trees are corrected by others. This is in stark contrast to a single Decision Tree, which is typically larger and more prone to overfitting. The 'small' trees in Random Forests contribute to a reduced variance in the model as they are trained on random subsets of the data, making the ensemble model more generalizable to unseen data.