## 3 Results and Discussion
### Validation of Various Methods on Independent Datasets
melanogaster_ species, the AUC value of the XGB4 mcPred implementation gains a slight boost compared to the other four state-of-the-art predictors, reaching a maximum of 97%, but is 1% less compared to the DeepTorrent predictor. For the independent datasets of _C. elegans_, _A. thaliana_, and _G. subterruneus_ species, the XGB4 mcPred predictor is above or on par with the 4 mcPredSVM, Meta-4 mCpred, and 4 mcPred-IFL predictors, but the AUC performance is slightly below that of the Deep4 mC and DeepTorrent predictors. For the independent dataset of _G. pickeringii_ species, our predictor reaches the same level as the two most advanced predictors currently available, Meta-4 mCpred and Deep4 mC, but with 9% less AUC values compared to the DeepTorrent predictor. In contrast, for the independent dataset of _E. coli_ species, the AUC value of our predictor is 9% higher than the Deep4 mC predictor and 2% higher than the DeepTorrent predictor. Figure 9: Performance comparison of XGB4 mcPred and other state-of-the-art predictors of the AUC value on independent datasets from multiple species. We attribute that there are two potential reasons for this, one being that traditional machine learning methods are still relatively difficult to learn contextual information about gene sequences compared with deep sequence models. Although we manually extracted many sequence preference features, they are indeed insufficient, compared to the features constructed through extensive exploration by the deep learning algorithm itself and combination of each sequence. However, there are also drawbacks to using deep learning techniques in that the features automatically extracted by the models are often uninterpretable or difficult to interpret clearly. Another reason is that the XGB4 mcPred predictor uses a much smaller benchmark dataset compared to the Deep4 mC and DeepTorrent predictors, which may limit the ability of the XGB4 mcPred predictor to learn more information during the training process. We also attribute the success of XGB4 mcPred to two factors, one of which is the clear preference of nucleotide sequences between the non-4 mC sites and 4 mC sites in these six datasets from multiple species according to the previous analysis. This study used One-Hot encoding to encode original DNA sequences directly, and also encode adjacent information and spaced information of different spaced sizes. Thus, we retained the most primitive and simplest sequence information. Subsequently, we used a two-step feature selection method to remove the redundant features after encoding, making the DNA sequence information representation of these features further improved. The other factor is that we used the XGBoost algorithm to learn these DNA site sequences. The core idea of the XGBoost algorithm is to obtain a new classifier by focusing the training on the DNA sequence information of the 4 mC sites misclassified by the existing base classifier, thus obtaining better results compared to other classifiers. Therefore, after the comparative analysis of the above components, we can conclude that our proposed XGB4 mcPred predictor will become an essential computational tool for identifying 4 mC sites.