There are different metrics; we are going to use the Model Certainty entropy score (Formula 1 below). This is a concept common in information theory. High entropy is a symptom of high uncertainty. This strategy is also known as uncertainty sampling ? and you Model Certainty can find the details in this blog titled Labeling with Active Learning ? which was first published in Data Science Central.
Formula Prediction Entropy Formula
Given a prediction for row x by the classification specific database by industry model ? we can retrieve a probability vector P(l/x) ? which sums up to 1 and shows the different n probability of a row to belong to a possible target class li. Using such a prediction vector ? we can measure its entropy score between 0 and 1 to define the uncertainty of the model in predicting P(l/x).
Wrapping up
In today’s episode ? we’ve taken a look at how model uncertainty can be used as a rapid way of moving our decision boundary to the correct position ? using as few labels as possible ? i.e. ? taking up as little time as possible with our expensive human-in-the-loop expert.
In the next blog in this series ? we will go on to use uncertainty how to work with digital marketing? sampling to exploit the key areas of the feature space to ensure an improvement of the decision boundary. Stay tuned.
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