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.

本研究探讨了基于提升树的主动学习方法对表格数据的有效性，利用模型不确定性进行样本选择，并针对回归任务提出了一种成本效益高的主动学习方法，同时也提出了改进的成本效益高的分类任务的主动学习方法。

基于增强树的基于模型不确定性的表格数据主动学习