Selected parameters for KNN, NN, and random forest algorithms
| Algorithm | The selected combination of parameters | Comments |
|---|---|---|
| KNN | k =15 | The number of closest neighbors for classification 1, 2 and 3 |
| k =20 | The number of closest neighbors for classification 4 and 5 | |
| NN | n_hidden = 3 | The number of hidden layers |
| h_size = (50, 50, 50) | Hidden layer size | |
| φ = tanh | Activation function [logistic, tahn, relu] | |
| α = 0.01 | Learning rate | |
| Random forest | max_depth = 100 | The maximum depth of the tree |
| max_features = sqrt | The number of features to consider when searching for a suitable split | |
| min_samples_split = 2 | The minimum number of samples required to split an internal node | |
| n_estimators = 100 | The number of trees in the forest |
| Algorithm | The selected combination of parameters | Comments |
|---|---|---|
| KNN | The number of closest neighbors for classification 1, 2 and 3 | |
| The number of closest neighbors for classification 4 and 5 | ||
| NN | n_hidden = 3 | The number of hidden layers |
| h_size = (50, 50, 50) | Hidden layer size | |
| Activation function [logistic, tahn, relu] | ||
| Learning rate | ||
| Random forest | max_depth = 100 | The maximum depth of the tree |
| max_features = sqrt | The number of features to consider when searching for a suitable split | |
| min_samples_split = 2 | The minimum number of samples required to split an internal node | |
| The number of trees in the forest |
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