Decision Trees (DTs) like LambdaMART have been one of the most effective types of learning-to-rank algorithms in the past decade. They typically work well with hand-crafted dense features (e.g., BM25 scores). Recently, Neural Networks (NNs) have shown impressive results in leveraging sparse and complex features (e.g., query and document keywords) directly when a large amount of training data is available. While there is a large chunk of work on how to use NNs for semantic matching between queries and documents, relatively less work has been conducted to compare NNs with DTs for general learning-to-rank tasks, where dense features are also available and DTs can achieve state-of-the-art performance. In this paper, we study how to combine DTs and NNs to effectively bring the benefits from both sides in the learning-to- rank setting. Specifically, we focus our study on personal search where clicks are used as the primary labels with unbiased learning- to-rank algorithms and a significantly large amount of training data is easily available. Our combination methods are based on ensemble learning. We design 12 variants and compare them based on two aspects, ranking effectiveness and ease-of-deployment, using two of the largest personal search services: Gmail search and Google Drive search. We show that direct application of existing ensemble methods can not achieve both aspects. We thus design a novel method that uses NNs to compensate DTs via boosting. We show that such a method is not only easier to deploy, but also gives comparable or better ranking accuracy.