Table 1 Parameter values of different machine learning models
From: Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning
Random forest | ||
|---|---|---|
Parameter | Grid search values | Optimal parameter value in our model |
n_estimators | 20, 40, 60, 80, 100, 120, 140, 160, 180, 200 | 80 |
min_samples_leaf | 1, 3, 5, 7, 9, 11, 13, 15 | 11 |
max_features | 0.1, log2, 0.25, sqrt, 1.0 | sqrt |
The remaining parameters are default values in the scikit-learn library | ||
XGBoost | ||
Parameter | Grid search values | optimal parameter value in our model |
n_estimators | 20, 40, 60, 80, 100, 120, 140, 160, 180, 200 | 100 |
learning_rate | 0.01, 0.02, 0.05, 0.1 | 0.1 |
gamma | 0, 0.1, 0.2, 0.5, 1.0 | 1.0 |
reg_lambda | 0, 1.0, 10.0 | 10 |
max_depth | 3, 4, 5, 6 | 4 |
colsample_bytree | 0.45, 0.5, 0.6, 0.7 | 0.45 |
subsample | 0.7, 0.8, 0.9 | 0.8 |
scale_pos_weight | 3, 4, 5 | 4 |
The remaining parameters are default values in the scikit-learn library | ||