Table 3 Performance of pricing model under different training-test ratio.
From: How to price a dataset: a deep learning framework for data monetization with alternative data
No. | Model | 80–20% | 70–30% | 90–10% | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
MSE | RMSE | MAE | MSE | RMSE | MAE | MSE | RMSE | MAE | ||
1 | MLR | 19.5889 | 4.4259 | 2.0271 | 51.3050 | 7.1627 | 2.7931 | 10.3752 | 3.2211 | 1.7959 |
2 | Lasso | 4.6022 | 2.1453 | 1.7631 | 4.5476 | 2.1325 | 1.7439 | 4.2012 | 2.0497 | 1.6848 |
3 | DT | 3.1783 | 1.1199 | 1.7828 | 3.4990 | 1.8706 | 1.1850 | 3.1492 | 1.7746 | 1.0538 |
4 | SVR | 6.8124 | 2.6101 | 2.1994 | 6.7321 | 2.5946 | 2.1813 | 5.7918 | 2.4066 | 1.9890 |
5 | MLP | 1.7074 | 1.3067 | 0.9235 | 1.5713 | 1.2535 | 0.8629 | 0.9355 | 0.9672 | 0.7165 |
6 | KNN | 3.5501 | 1.8842 | 1.3464 | 3.5715 | 1.8898 | 1.3833 | 3.3319 | 1.8253 | 1.3472 |
7 | GBDT | 1.8630 | 1.3649 | 1.0129 | 1.8482 | 1.3595 | 1.0199 | 1.5875 | 1.2600 | 0.9567 |
8 | LSTM | 2.9336 | 1.7128 | 1.3407 | 4.1243 | 2.0308 | 1.6152 | 3.6693 | 1.9155 | 1.5013 |
9 | RF | 1.1682 | 1.0808 | 0.7661 | 1.3727 | 1.1716 | 0.8350 | 1.0718 | 1.0353 | 0.7284 |
10 | LGBM | 0.9941 | 0.9970 | 0.6489 | 1.1497 | 1.0722 | 0.7266 | 0.8526 | 0.9234 | 0.6389 |
