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Table 5 Parameters of the random forest models

From: Predicting response to CCRT for esophageal squamous carcinoma by a radiomics-clinical SHAP model

Model

n_estimatorsa

max_depthb

max_featuresc

random_stated

Clinical

6

5

1

2023

TNM

7

3

2

2023

CTV

126

2

1

2023

GTV

17

1

2

2023

CTV-Clinical

1

1

1

2023

GTV-Clinical

7

8

1

2023

  1. an_estimators, the number of trees in the forests
  2. bmax_depth, the maximum depth of the tree
  3. cmax_features, the number of features to consider when looking for the best split
  4. drandom_state, random state instance to control the reproducibility of the bootstrapping of samples and features
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BMC Medical Imaging

ISSN: 1471-2342

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