In addition to committee based active learning for classification, modAL also implements committee based regression. This is done with the CommitteeRegressor class.
Differences between Committee and CommitteeRegressor¶
From an API viewpoint, there are two main differences between the CommitteeRegressor and the Committee classes. First, CommitteeRegressor doesn’t have
.predict_proba() methods, since regressors in general doesn’t provide a way to estimate the probability of correctness. (One notable exception is the Gaussian process regressor.)
The other main difference is that now you can pass the argument
return_std=True for the method
.predict(), which in this case
will return the standard deviation of the prediction. This follows the scikit-learn API for those regressor objects which the standard deviation can be calculated.
With an ensemble of regressors like in the CommitteeRegressor model, a measure of disagreement can be the standard deviation of predictions, which provides a simple way to query for labels. This is not the case in general: for ordinary regressors, it is difficult to come up with viable query strategies because they don’t always provide a way to measure uncertainty. (One notable exception is the Gaussian process regressor.)
This is demonstrated in this example, where two regressors are trained on distinct subsets of the same dataset. In the figure below, the regressors are shown along with the mean predictions and the standard deviation.