Table 2 The parameters and set of values for various off-the-shelf baselines
From: Machine learning predicts nucleosome binding modes of transcription factors
Method | Tuning parameters |
|---|---|
Logistic regression | The norm used in the penalization: none, L1, L2, elastic net Regularization coefficient: 100, 10, 1, 0.1, 0.01 Subsequence length: 3, 4, 5, 6 |
k-nearest neighbors | Number of neighbors to use: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 Contribution of members in the neighborhood: uniform, distance Distance metric: Euclidean, Manhattan, Minkowski Subsequence length: 3, 4, 5, 6 |
Support vector machine | Kernel: Linear, Polynomial, RBF, Sigmoid Regularization parameter (C): 50, 10, 1.0, 0.1, 0.01 Subsequence length: 3, 4, 5, 6 |
Random forest | The number of features to consider when looking for the best split: sqrt(num features), log2(num features) The number of trees in the forest: 10, 100, 200, 500, 1000 Subsequence length: 3, 4, 5, 6 |