How can I implement active learning in my machine learning project?

How can I implement active learning in my machine learning project?

Active learning is a machine learning approach where the model selects the most informative data points to learn from. You can implement it by using a loop where the model queries the user for labels on uncertain predictions.

What are some strategies for selecting the most informative data points?

Common strategies include uncertainty sampling, query-by-committee, and expected model change. Each has its own advantages depending on your specific use case.

Can you give me an example of uncertainty sampling?

Sure! In uncertainty sampling, you train your model on a small labeled dataset, then use it to predict labels for the unlabeled data. You select the instances where the model is least confident (e.g., predictions close to 0.5 in binary classification) and query the user for their true labels.

How do I evaluate the performance of my active learning model?

You can evaluate performance by comparing the model's accuracy on a validation set against the number of labeled instances used. Metrics like precision, recall, and F1-score are also useful.