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Simulations, Data Analysis and Algorithms

Table 4 Summary of hyperspaces for the data-driven models investigated in this work. For the GP, MLP, and XGB models, the optimization algorithm (see Sect. 3.5) searches these spaces over 100 iterations to identify the most performant hyperparameter set for each model

From: Machine learning applied to simulations of collisions between rotating, differentiated planets

Method Hyperparameter Range
MLP Number of layers {1,2,3}
Neurons per layer {1,2,…,24}
GP Kernel Constant, Matérn 3/2, rational quadratic, radial-basis functions
Noise (α) [0,10−2]
Kernel restart {0,1,…,5}
XGB Number of estimators {1,10,…,1000}
Maximum tree depth {3,4,…,12}
Column subsample ratio {0.5,…,1}
PCE Polynomial order {2,3,…,15}
q-norm {0.5,0.6,…,1.0}
Maximum interaction {2,3,…,5}
Feature importance =0.01