自动选出最少必要特征，兼顾精度与效率。

Feature selection (FS) is a fundamental challenge in machine learning, particularly for high-dimensional tabular data, where interpretability and computational efficiency are critical. Existing FS methods often cannot automatically detect the number of attributes required to solve a given task and involve user intervention or model retraining with different feature budgets. Additionally, they either neglect feature relationships (filter methods) or require time-consuming optimization (wrapper methods). To address these limitations, we propose AutoNFS, which combines the FS module based on Gumbel-Sigmoid sampling with a predictive model evaluating the relevance of the selected attributes. The model is trained end-to-end using a differentiable loss and automatically determines the minimal set of features essential to solve a given downstream task. Unlike many wrapper-style approaches, AutoNFS introduces a low and predictable training overhead and avoids repeated model retraining across feature budgets. In practice, the additional cost of the masking module is largely independent of the number of input features (beyond the unavoidable cost of processing the input itself), making the method scalable to high-dimensional tabular data. We evaluate AutoNFS on well-established classification and regression benchmarks as well as real-world metagenomic datasets. The results show that AutoNFS is competitive with, and often improves upon, strong classical and neural FS baselines while selecting fewer features on average across the evaluated benchmarks.