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, gammaā=ā0.20 |
Gamma: 0.05. 0.10, 0.20 | Litecoin: radial kernel, gammaā=ā0.05 | |
Ethereum: radial kernel, gammaā=ā0.10 | ||
SVM-binary | Kernel: radial, linear, polynomial | Bitcoin: radial kernel, gammaā=ā0.20 |
Gamma: 0.05. 0.10, 0.20 | Litecoin: polynomial kernel, gammaā=ā0.10 | |
Ethereum: radial kernel, gammaā=ā0.10 |