Research Article
Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System
Table 1
Results for the training and validation data (DTREG software).
| Estimators of the model accuracy (DTREG) | Value |
| Training data | Mean target value for input data | 13.358369 | Mean target value for predicted values | 13.356284 | Variance in input data | 4.4677631 | Residual variance after model fit | 0.0024144 | Proportion of variance explained by model R2 | 0.99946 (99.946%) | Coefficient of variation (CV) | 0.003678 | Normalized mean square error (NMSE) | 0.000540 | Correlation between actual and predicted R | 0.999731 | Maximum error | 0.3219897 | RMSE (root mean squared error) | 0.0491365 | MSE (mean squared error) | 0.0024144 | MAE (mean absolute error) | 0.0288461 | MAPE (mean absolute percentage error) | 0.2199448 |
| Validation data | Mean target value for input data | 13.358369 | Mean target value for predicted values | 13.35876 | Variance in input data | 4.4677631 | Residual variance after model fit | 0.0199458 | Proportion of variance explained by model R2 | 0.99554 (99.554%) | Coefficient of variation (CV) | 0.010572 | Normalized mean square error (NMSE) | 0.004464 | Correlation between actual and predicted R | 0.997882 | Maximum error | 0,5402981 | RMSE (root mean square error) | 0.1412296 | MSE (mean square error) | 0.0199458 | MAE (mean absolute error) | 0.0984472 | MAPE (mean absolute percentage error) | 0.7326534 |
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