Learning Paradigms

Active Learning

In shortA training approach where the model picks the unlabelled examples it is least sure about for a human to label, so you reach good accuracy with fewer labels.

Updated 24 Oct 2026· 2 min read

Active learning is a way of training a supervised machine learning model when labelled data is expensive to produce. Instead of labelling a large random sample up front, the model repeatedly chooses the examples it would learn most from and asks a person to label just those. The aim is the same accuracy with far less labelling effort.

How it works

Active learning runs as a loop with a human in it:

Start with a small labelled seed set and train a first model.
Run that model over a pool of unlabelled examples and score how uncertain it is about each one (for example, a predicted probability close to 50/50, or two classes with almost the same score).
Send the most informative examples to a human annotator. This selection rule is called the query strategy; uncertainty sampling is the most common one.
Add the new labels, retrain, and repeat until accuracy stops improving or the labelling budget runs out.

The model is effectively telling you where it is guessing, which is the same signal MLforSEO teaches you to read from a low confidence score in classification output.

How it’s used in SEO and marketing

MLforSEO mentions active learning in the context of custom-training entity extraction models: tools such as spaCy (and its companion annotation tool, Prodigy) let the model pick the most informative examples to label, cutting the effort of building a domain-specific model. Typical marketing uses:

Teaching an entity model to recognise your own product names, features or niche jargon that general pre-trained models label as “other”.
Building a custom content or search intent classifier where only a few hundred keywords can realistically be labelled by hand.
Labelling reviews, support tickets or social posts for a bespoke taxonomy (for example complaint type) without reading thousands of them.
Reviewing the low-confidence predictions of an existing classifier and feeding the corrections back in.

Example

You want to tag 20,000 blog posts with your own five-topic taxonomy. You label 100 posts, train a quick classifier and score the remaining 19,900. The model is confident about most of them but splits almost evenly between two topics on 300 posts. You label 50 of those borderline posts first, retrain, and repeat. After a few rounds you have a usable classifier from a few hundred labels, and the posts you read were the genuinely ambiguous ones, which also tells you where your taxonomy itself may need clarifying.

Limitations and things to watch

It needs a human in the loop and a tool that supports the cycle, so it suits ongoing projects more than one-off analyses.
Uncertainty sampling can keep selecting outliers or noisy examples; mixing in some random samples keeps the training set representative.
The selected examples are biased towards hard cases, so evaluate the model on a separate, randomly drawn test set.
For beginners, MLforSEO’s advice is that a pre-trained API (such as the Google Cloud Natural Language API) is often the faster starting point before investing in custom training.