Huawei Certified ICT Associate – Artificial Intelligence (HCIA-AI) Practice Exam 2025 - Free HCIA-AI Practice Questions and Study Guide

In machine learning, integrated learning policies refer to techniques that create a model by combining multiple base learners to improve the overall performance and robustness of predictions. Stacking, bagging, and boosting are all well-established ensemble methods that involve combining the outputs of several models to achieve better accuracy.

Stacking involves training a new model to combine the predictions of several base models. Bagging, short for bootstrap aggregating, works by training multiple copies of the same algorithm on different subsets of the training data to reduce variance. Boosting focuses on training models sequentially, where each new model is trained to correct the errors made by the previous ones, ultimately leading to a stronger overall model.

Marking, on the other hand, is not recognized as an integrated learning policy in machine learning algorithms. The concept of "marking" does not correspond to any known technique for combining models or improving performance, making it the correct answer to the question of which option is not an integrated learning policy.