weak learners

In machine learning weak learners are sub-models that are in themselves not that good, but can potentially be combined, or modified in someway to make them stronger. For example, random forest techniques create large numbers of decision trees each of which may not be that effective, but when combined by higher-level decision algorithm (such as weighted voting), have proven very powerful.