Table 7 Quantitative values of performance measures such as MSE, MAE, MAPe, MeAE, MPD, and \(R^{2}\) of several competing ML Regression Models viz. Linear, Bayes Ridge, Support Vector, Decision Tree, Random Forest, Lars, LassoCV, LassoLarsCV, Multi Layer Perceptron, Bagging, and Adaboost.

From: Enhanced slope stability prediction using ensemble machine learning techniques

Regression models

Performance measures

MSE

MAE

MAPE

MeAE

MPD

\(R^{2}\)

All features [train:test=70:30]

Linear regression

0.1834

0.3266

0.2898

0.2674

0.1414

0.7482

Bayes ridge regression

0.1517

0.2987

0.3266

0.2284

0.1167

0.7565

Elastic net regression

0.5583

0.5457

0.5718

0.3812

0.3333

0.1863

Support vector regression

0.2019

0.2126

0.1339

0.1227

0.0710

0.7737

Decision tree regression

0.2796

0.3374

0.2776

0.1850

0.1459

0.7115

Random forest regression

0.2538

0.3266

0.2808

0.1981

0.1212

0.8032

Lars regression

0.1918

0.3338

0.2543

0.2490

0.1224

0.7943

LassoCV regression

0.4136

0.4489

0.3067

0.2853

0.1810

0.7004

LassoLarsCV regression

0.1781

0.3070

0.2536

0.2508

0.0998

0.8412

MLP regression

0.1629

0.2840

0.2187

0.1852

0.0814

0.8364

Bagging regression

0.0782

0.2060

0.1885

0.1395

0.0580

0.8450

Adaboost regression

0.2824

0.3914

0.3385

0.2892

0.1549

0.7461

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