  Supervised machine learning relies on the availability of good labelled data
for model training. Labelled data is acquired by human annotation, which is a
cumbersome and costly process, often requiring subject matter experts. Active
learning is a sub-field of machine learning which helps in obtaining the
labelled data efficiently by selecting the most valuable data instances for
model training and querying the labels only for those instances from the human
annotator. Recently, a lot of research has been done in the field of active
learning, especially for deep neural network based models. Although deep
learning shines when dealing with image\textual\multimodal data, gradient
boosting methods still tend to achieve much better results on tabular data. In
this work, we explore active learning for tabular data using boosted trees.
Uncertainty based sampling in active learning is the most commonly used
querying strategy, wherein the labels of those instances are sequentially
queried for which the current model prediction is maximally uncertain. Entropy
is often the choice for measuring uncertainty. However, entropy is not exactly
a measure of model uncertainty. Although there has been a lot of work in deep
learning for measuring model uncertainty and employing it in active learning,
it is yet to be explored for non-neural network models. To this end, we explore
the effectiveness of boosted trees based model uncertainty methods in active
learning. Leveraging this model uncertainty, we propose an uncertainty based
sampling in active learning for regression tasks on tabular data. Additionally,
we also propose a novel cost-effective active learning method for regression
tasks along with an improved cost-effective active learning method for
classification tasks.

Machine Learning