氢化物超导体临界转变温度的机器学习模型

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Machine Learning Model for Predicting the Critical Transition Temperature of Hydride Superconductors

ZHAO Jinbin, WANG Jiantao, HE Dongchang, LI Junlin, SUN Yan, CHEN Xing-Qiu, LIU Peitao

表1 随机森林算法在sklearn库中使用的超参数(其他未明确指出的参数使用了默认值)

Table 1 Hyper-parameters used in the sklearn library for the random forest algorithm (For other parameters that are not explicitly specified, default values are used)

Parameter Meaning of parameter Value n-estimators Number of decision trees 10-30 Max-depth Maximum depth of decision trees None, 1, 2, 4 Min-samples-split Minimum number of samples required to split internal nodes 2, 4, 8 Min-samples-leaf Minimum number of samples at leaf nodes 1, 2, 4, 8