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Table 8 Overview of the parameter space screened during HP tuning of the baseline MLP model. The HP were tuned for each dataset using four different HP metrics. The assess the performance on the validation set, the average of ten model repetitions was obtained. The overview presents the range of HP explored during the tuning process

From: Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models

Model

Hyperparameter

Explored Space

Baseline MLP

Learning rate

{1e-5, 1r-4, 1e-3, 1e-2, 0.1}

Hidden size

{5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130}

Weight decay

{1e-5, 1r-4, 1e-3, 1e-2, 0.1}

Dropout

{0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9}