## 6 Example Validation
### Analysis of Results  
In this paper, MAE, RMSE and \(R^{2}\) are chosen as the evaluation metrics of target prediction results, and the smaller MAE and RMSE, and the closer \(R^{2}\) is to 1 means the target prediction quality is better. (1) Comparing the metrics results of target prediction. In the Piping dataset, the MAE results obtained by O-AE are the best, the RMSE and \(R^{2}\) results obtained by O-AE-b are the best, and the MAE results of O-AE-b are the best among the seven experiments except for O-AE. The RMSE and \(R^{2}\) results of O-AE are also the best among the seven experiments except for O-AE-b. The results of all three metrics for TE are worse in the other experiments. In the Hull structure dataset, the MAE, RMSE, and \(R^{2}\) results obtained by O-AE-b are the best, and the RMSE and \(R^{2}\) results obtained by O-AE are also the best among the seven experiments except for O-AE-b. The results of all three metrics for TE are worse in the other experiments. In the Gait dataset, the MAE results obtained by 5-EE are the best, the RMSE and \(R^{2}\) results obtained by O-AE-b are the best, and the MAE results of O-AE-b are the best among the seven experiments except for 5-EE. The RMSE and \(R^{2}\) results of O-AE are also the best among the seven experiments except for O-AE-b. The results of all three metrics for TE are worse in the other experiments. In the Fish dataset, the MAE results obtained by OHE are the best, the RMSE and \(R^{2}\) results obtained by O-AE-b are the best, and the MAE results of O-AE-b are the best among the seven experiments except for OHE. The RMSE and \(R^{2}\) results of O-AE are also the best among the seven experiments except for O-AE-b. The results of all three metrics for TE are worse in the other experiments. In particular, the prediction results of the seven experiments on the Restaurant dataset are not very satisfactory, with the highest \(R^{2}\) of only 0.7624, which is analyzed in this paper as a possible reason for the fact that the chosen BP neural network model is not applicable to the Restaurant dataset. However, in the Restaurant dataset, the MAE results obtained by O-AE are the best, the RMSE and \(R^{2}\) results obtained by O-AE-b are the best, and the MAE results of O-AE-b are the best among the seven experiments except for O-AE. The RMSE and \(R^{2}\) results of O-AE are also the best among the seven experiments except for O-AE-b. The results of all three metrics for 1-EE are worse in the other experiments. Overall, the Bayesian optimization of the hyperparameters of the autoencoder for O-AE as well as the basic O-AE have consistently excellent target prediction results when experiments are carried out on different datasets. Analyzing the metric results, the basic O-AE can then meet the encoding needs of unordered multi-categorical variables, while the MAE results of O-AE are better than the MAE results of O-AE-b in some datasets. (2) Comparing the experimental performance of different methods. In other experiments, 1-EE and 5-EE are embedding methods with one and five embedding dimensions, respectively, and the metric results of 1-EE and 5-EE are in the middle of the range on the five datasets, with little difference in experimental performance. The metric results of OHE on the five datasets are also more stable, and the experimental performance is a little bit poorer than that of O-AE and O-AE-b, but OHE has the problems of unordered multi-categorical variables with larger dimensions and sparse data after encoding. The experimental performance of LE and TE on the five datasets is general mainly because the use of LE introduces additional order misclassification and distance bias in addition to the originally unordered features, and TE affects the fitting ability of the neural network model. Overall, the experimental results of O-AE-b and O-AE are superior compared to other classical encoding methods.