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Table 4 Parameters tested in the ML models and parameters leading to the best model

From: Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Model Hyperparameters used Hyperparameters of the model with the best performance in the validation sample
RF Number of trees: 500, 1000, 1500 Bitcoin: 1500 trees, 50.0% of the variables sampled at each split
Percentage of variables sampled at each split: 50.0%, 33.3% Litecoin: 1500 trees, 50.0% of the variables sampled at each split
Ethereum: 500 trees, 50.0% of the variables sampled at each split
RF-binary Number of trees: 500, 1000, 1500 Bitcoin: 500 trees, 33.3% of the variables sampled at each split
Percentage of variables sampled at each split: 50.0%, 33.3% Litecoin: 1000 trees, 33.3% of the variables sampled at each split
Ethereum: 1500 trees, 50.0% of the variables sampled at each split
SVM Kernel: radial, linear, polynomial Bitcoin: polynomial kernel, gamma = 0.20
Gamma: 0.05. 0.10, 0.20 Litecoin: radial kernel, gamma = 0.05
Ethereum: radial kernel, gamma = 0.10
SVM-binary Kernel: radial, linear, polynomial Bitcoin: radial kernel, gamma = 0.20
Gamma: 0.05. 0.10, 0.20 Litecoin: polynomial kernel, gamma = 0.10
Ethereum: radial kernel, gamma = 0.10
  1. This table presents all combinations of hyperparameters used in the experiments. The hyperparameters of the model with the best performance in the validation sample were then used to define the trading strategies in the test sample. For Random Forests (RF), the remaining hyperparameters were kept at the defaults of the randomForest R package. For Support Vector Machines (SVM), the remaining hyperparameters were kept at the defaults of the e1071 R package