Table 1 Comparison of the predictive performance of XGBoost with cross validation, Random Forest without cross validation and the RCA benchmark for the activations using different indicators.

From: Product progression: a machine learning approach to forecasting industrial upgrading

Algorithm

XGBoost-CV

Random Forest

RCA

AUC-ROC

0.698

0.724

0.592

F1 score

0.0479

0.0476

0.0369

mean Precision@10

0.059

0.045

0.039

Precision

0.34

0.035

0.023

Recall

0.079

0.073

0.103

MCC

0.043

0.042

0.035

AUC-PR

0.018

0.017

0.011

Accuracy

0.981

0.982

0.967

Negative predictive value

0.994

0.994

0.994

TP

202

186

263

FP

5663

5063

11413

FN

2359

2375

2298

TN

403767

404367

398017

Computational cost

 100

1

–

  1. The last row indicates the computational cost with respect to the non cross validated Random Forest; XGBoost is about 100 times slower.
  2. The highest values of each indicator are in bold.
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