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Table 1 Hyperparameters of machine learning models

From: Prediction of the risk of developing end-stage renal diseases in newly diagnosed type 2 diabetes mellitus using artificial intelligence algorithms

Model

Hyperparameters

Range

Optimal values

Logistic regression

penalty

[l1, l2]

l2

Cs

[0.001, 0.1, 1, 100, 1000]

1

Extra trees

min_samples_leaf

[5, 8, 10]

5

criterion

[gini, entropy, log_loss]

entropy

max_features

[sqrt, log2, none]

sqrt

Random forest

max_depth

[3, 5, 10]

10

min_samples_split

[2, 5, 10]

5

GBDT

learning_rate

[0.01, 0.1, 0.2]

0.2

max_depth

[3, 5, 8]

8

n_estimators

[10, 20]

20

XGBoost

gamma

[0.5, 1, 5]

0.5

colsample_bytree

[0.6, 0.8, 1.0]

1.0

max_depth

[3, 4, 5]

5

LGBM

n_estimators

[8, 16, 24]

24

num_leaves

[6, 12, 16]

16

max_bin

[255, 510]

510

  1. Abbreviations: GBDT gradient boosting decision tree, XGBoost extreme gradient boosting, LGBM light gradient boosting machine