To use fewer labels ? we need data points positioned around the We then identify decision boundary ? as these are the data points best defining the line we are looking for. But how do we find them ? not knowing where this decision boundary lies? The answer is ? we use model predictions — and ? to be more precise — we use model certainty.
Figure In the 2D dimensional feature space ? the dotted We then identify decision boundary belongs to the model trained in the current iteration k. To move the decision boundary in the right direction ? we use uncertainty sampling ? asking the user to label new data points near to the current decision boundary. ? which subsequently leads to a better decision boundary in the next iteration after the model is retrained.
Looking for Misclassification Using Uncertainty
At each iteration ? the decision boundary moves when a recent mobile phone number data new point is labeled contradicting the model prediction. The intuition behind model certainty is that a misclassification is more likely to happen when the model is uncertain of its prediction. When the model has already achieved decent performance ? model uncertainty is symptomatic of misclassification being more probable ? i.e. ? a wrong prediction. In the feature space ? model uncertainty understand what a forensic science professional does increases as you get closer to the decision boundary. To quickly move our decision boundary to the right position ? we ? therefore ? look for misclassification using uncertainty. In this manner ? we select data points that are close to the actual decision boundary (Figure 2).
So here we go
At each iteration ? we score all unlabeled data points with the retrained angola latest email list model. Next ? we compute the model uncertainty ? take the top uncertain predictions ? and ask the user to label them. By retraining the model with all of the corrected predictions ? we are likely to move the decision boundary in the right direction and achieve better performance with fewer labels.