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Table 1 Comparison of Lasso, Random Forests, and MLP-SAE model

From: A deep auto-encoder model for gene expression prediction

Method Hyperparameter Hyperparameter value MSE
Lasso α 0.05 0.3516
   0.1 0.3182
   0.2 0.3002
   0.3 0.2951
   0.4 0.2930
   0.5 0.2918
   0.6 0.2914
   0.7 0.2912
   0.8 0.2912
Random forests Number of estimators 10 0.3221
   20 0.3127
   30 0.3080
   40 0.3001
   50 0.2989
   60 0.3003
   70 0.2986
   100 0.3003
   150 0.2974
   200 0.2967
MLP-SAE model Learning rate 0.1 0.2890
   0.01 0.2909
   0.001 0.2895
   0.0001 0.2908
   0.00001 0.2918
  1. Each row represents the hyperparameter used and corresponding MSE for each hyperparameter setup of each model. Bold rows denote the hyperparameters and corresponding MSE for the optimal models of the three methods respectively