A machine learning approach where the model queries the user or an oracle for input on uncertain data.
More about Active Learning:
Active Learning is a semi-supervised machine learning technique. Instead of passively receiving all training data, the model actively queries for the most informative data points when faced with uncertainty. This approach can reduce the amount of labeled data required for effective learning, as the model focuses on the most ambiguous and informative instances.
In the context of chatbots, active learning can help in refining responses by seeking feedback on uncertain interactions or by prioritizing the annotation of specific user queries.
Frequently Asked Questions
How does Active Learning reduce the need for labeled data?
By focusing on the most uncertain data points, active learning ensures that the model receives the most informative examples. This can lead to faster convergence and improved performance with less labeled data compared to traditional methods.
Is human intervention always required in Active Learning?
While not always, active learning often involves human experts to label or clarify the data points the model finds ambiguous, ensuring more accurate learning from those instances.
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