Table 4 Model’s generalization capability between different regions.

From: Measuring and classifying IP usage scenarios: a continuous neural trees approach

Region

Sichuan \(\rightarrow\) Chongqing

Chongqing \(\rightarrow\) Sichuan

Shandong \(\rightarrow\) Sichuan

Shandong \(\rightarrow\) Chongqing

Metric

Precision

Recall

Auc

Precision

Recall

Auc

Precision

Recall

Auc

Precision

Recall

Auc

SVM

0.6714

0.6725

0.8885

0.7993

0.7457

0.9382

0.8671

0.7530

0.9390

0.9114

0.7777

0.9645

NB

0.5641

0.5655

0.8650

0.6524

0.6590

0.8861

0.6726

0.6167

0.8928

0.7575

0.6547

0.9270

LDA

0.6809

0.6412

0.8208

0.6621

0.6417

0.8701

0.6606

0.5824

0.8878

0.8036

0.6134

0.9258

RF

0.6246

0.7799

0.9377

0.7709

0.7059

0.9106

0.8777

0.7098

0.8928

0.8930

0.7984

0.9632

XgBoost

0.6051

0.7543

0.9376

0.7360

0.6628

0.9118

0.8770

0.7065

0.9431

0.9001

0.8125

0.9468

CatBoost

0.6137

0.7697

0.9038

0.6136

0.4891

0.8345

0.7400

0.6616

0.8387

0.7589

0.7403

0.8876

TabNet

0.6336

0.7797

0.9158

0.7326

0.6936

0.9054

0.8063

0.7327

0.9097

0.8261

0.7920

0.9349

NON

0.6838

0.7384

0.8825

0.7097

0.7264

0.9041

0.8096

0.7639

0.9191

0.8013

0.8164

0.9267

AutoInt

0.5887

0.7402

0.8250

0.6802

0.6232

0.7333

0.8618

0.6741

0.7856

0.8384

0.7623

0.8212

NODE

0.6315

0.7778

0.9145

0.7321

0.6920

0.9034

0.8051

0.7319

0.9025

0.8245

0.7911

0.9335

ODTSR

0.7295

0.8042

0.9462

0.8409

0.7714

0.9444

0.8817

0.7679

0.9582

0.9322

0.8654

0.9763