"Maximizing Learning Efficiency with Active Learning"
Active Learning in Machine LearningActive Learning is a type of machine learning where the algorithm is able to select which data it wants to learn from. In traditional machine learning, the algorithm is given a set of labeled data and it learns from that data. However, in active learning, the algorithm is able to ask for more data to learn from. Active learning is needed when there is a limited amount of labeled data available. In many cases, labeling data can be time-consuming and expensive. Active learning allows the algorithm to learn from a smaller amount of labeled data by selecting the most informative data points to learn from. Active learning should be used when the cost of labeling data is high, when there is a limited amount of labeled data available, or when the algorithm needs to be updated frequently. There are several algorithms for active learning, including:
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