Table 3 Performance comparison on Kanpur Nagar dataset.

From: A meta-learning ensemble framework for robust and interpretable prediction of emergency medical services demand

Metric

Variant

MLP

SVR

RF

XGB

AHELM

TBLSSVR

MHKLDMR

EM-LR

MAE

T

3.4939

5.4019

3.5409

3.5686

3.2891

5.8344

3.7247

3.1295

T+W

3.4751

4.7037

3.6260

3.8020

4.3143

9.9660

4.5408

3.1530

T+W+FS

3.3823

3.8566

3.6643

3.7375

3.3577

5.7006

3.4765

3.1064

RMSE

T

4.6885

6.8488

4.8538

4.9119

4.2275

6.9429

4.6651

4.2251

T+W

4.7668

6.0298

4.9848

5.1385

5.8529

11.2331

5.9643

4.2890

T+W+FS

4.5576

5.1448

5.0053

5.0545

4.3494

7.2375

4.5425

4.2088

MAPE

T

0.6064

4.7768

0.5751

0.5841

0.4197

0.6325

0.4712

0.4199

T+W

0.6008

5.8697

0.6058

0.6480

0.2998

0.8185

0.4459

0.4387

T+W+FS

0.5472

1.4932

0.6185

0.6376

0.4791

0.5309

0.4725

0.4226

MBE

T

– 1.8477

– 4.9832

– 2.3784

– 2.5247

– 1.5338

– 3.9967

– 1.7092

– 0.7667

T+W

– 1.9244

– 3.4966

– 2.5995

– 2.8067

0.1807

– 9.9408

– 2.0068

– 0.9963

T+W+FS

– 1.7471

– 1.8809

– 2.6064

– 2.6814

– 0.1909

– 5.3237

– 0.5884

– 0.8628