  Neural architecture search (NAS) with an accuracy predictor that predicts the
accuracy of candidate architectures has drawn increasing attention due to its
simplicity and effectiveness. Previous works usually employ neural
network-based predictors which require more delicate design and are easy to
overfit. Considering that most architectures are represented as sequences of
discrete symbols which are more like tabular data and preferred by non-neural
predictors, in this paper, we study an alternative approach which uses
non-neural model for accuracy prediction. Specifically, as decision tree based
models can better handle tabular data, we leverage gradient boosting decision
tree (GBDT) as the predictor for NAS. We demonstrate that the GBDT predictor
can achieve comparable (if not better) prediction accuracy than neural network
based predictors. Moreover, considering that a compact search space can ease
the search process, we propose to prune the search space gradually according to
important features derived from GBDT. In this way, NAS can be performed by
first pruning the search space and then searching a neural architecture, which
is more efficient and effective. Experiments on NASBench-101 and ImageNet
demonstrate the effectiveness of using GBDT as predictor for NAS: (1) On
NASBench-101, it is 22x, 8x, and 6x more sample efficient than random search,
regularized evolution, and Monte Carlo Tree Search (MCTS) in finding the global
optimum; (2) It achieves 24.2% top-1 error rate on ImageNet, and further
achieves 23.4% top-1 error rate on ImageNet when enhanced with search space
pruning. Code is provided at https://github.com/renqianluo/GBDT-NAS.
