Hyperparameter search space table.

The best set of parameters is {number of conv filters, Nc: 256, number of units in LSTM Nh: 64, dropout probability for Dropout Layer: 0, learning rate: 0.001} for read-level prediction on 5-fold cross validation of training data. The window size of convolutional layers, W, is set to 9 and the number of hidden nodes in attention layer, Na, is set to 16 in the default setting. Cole et al. found that the Naïve Bayes classifier performs similarly when using 8-mer and 9-mer as features but slightly worse using smaller k-mers [84] for the taxonomic classification of 16S rRNA reads. Therefore, we similarly use a window size of 9 for convolutional filters. In S11 Appendix, we further show that sample-level classification performance of the model is generally insensitive to the window size. In future work, we will consider other consequences of altering window size, such as computational performance and interpretability.
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