融合全监督学习的半监督矿石粒度预测算法

姜志宏,陈澳

Semi-supervised Ore Granularity Prediction Algorithm Incorporating Fully Supervised Learning

Zhihong JIANG,Ao CHEN

表2 各模型的最佳超参数及预测误差

Table 2 Optimal hyperparameters and prediction errors for each model

模型	最佳超参数	RMSE	MAE
Tree	max_depth = 4	0.030	0.026
RF	n_estimators = 20	0.040	0.027
GBDT	learning_rate = 0.01 max_depth = 5 n_estimators = 200	0.026	0.022
XGBoost	gamma = 0.0 max_depth = 4 min_child_weight = 4 n_estimators = 70	0.040	0.028
Bayes	alpha_1= 1e-08 alpha_2= 1e-06 lambda_1= 1e-06 lambda_2= 1e-08 n_iter= 100	0.029	0.022
多项式回归	Linearregression_fit _intercept= True polynomialfeatures_degree2	0.080	0.051
岭回归	Alpha = 0.3 gamma = 0.1 kernel = linear	0.030	0.023
SVM	C = 1.0 Gamma = 1.0 Kernel = linear	0.075	0.063
BP神经网络	Activation = relu Alpha = 0.0001 hidden_layer_sizes = （100，）	0.030	0.023