# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 4 Findings and Discussions  
The first output (please refer to Table 5) from the supervised learning process focused on the feasibility of model creation based on volume and the diverse values of the UE's attributes. Partial instances (based on splitting percentage) from the entire dataset were used to train and create the classification model. The finding from this stage demonstrated that a high volume of datasets composed of durations and number of UEs produced better model accuracy. The accuracy of the multi-output classification model was determined by the percentage of the total number of wrong classification outputs against actual values from offline clustering and resource assignment processes using the MILP method. The table below stipulates the pattern showing the result of the supervised classification learning model. The model's performance improved by producing a minimum error percentage according to the trained dataset's expansion. In both downlink and uplink streams, the model performs better in the increment volume of data. Subsequently, the study assesses the practicality of each created model to classify the targeted variables on the other dataset. Referring to Table 6, the model's performance on each dataset varies. The classification model developed from Set 1.1 consists of only 60 instances that produce a significant number of errors (unmatched output of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No** & **Dataset, _i_** & **ML Model, M** & **Actual** & **ML Classifier** \\ \hline
1 & Set 1.1 & & & & & & \\ \hline
2 & Set 1.2 & & & & & & \\ \hline
3 & Set 1.3 & & & & & & \\ \hline
4 & Set 5.1 & & & & & & \\ \hline
5 & Set 5.2 & & & & & & \\ \hline
6 & Set 5.3 & & & & & & \\ \hline
7 & Set 10.1 & & & & & & \\ \hline
8 & Set 10.2 & & & & & & \\ \hline
9 & Set 10.3 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The application of ML models on every dataset. Figure 3: The summary of the total number of UEs generated from the simulation. classification model against actual), and the second classification target value, the assigned transport network, shows more than 70% unmatched output. Likewise, the classification model developed based on the massive volume of data in Set 10.1, consisting of 600 instances, produces close to 60% unmatched output. The second model, developed with 300 instances, sits in the middle range between model Set 1.1 and Set 10.1 and shows good classification output. However, the percentage errors for the second classification target (assigned transport network) are higher than the first target output (cluster number). Overall performance of the model developed from Set 5.1 shows full accuracy when classifying the \"cluster number\" for downlink and uplink streams, whilst the classification for \"assigned transport network\" gives the average of 7.37% and 6.55%, respectively. The value translates to a scenario where in 100 traffic-transport assignment decisions, eight traffic from the downlink stream and seven traffic from the uplink stream will wrongly be assigned to the non-optimum transport network. The output from upstream links shows low variations of errors across all models developed and tested to respective datasets. The average error percentage for all datasets shows that the model developed using set 10.1 gives more than 95% classifying accuracy. Referring to the table, the highlighted red cells indicate the model's performance against its training dataset; hence, it produces no error. The overall process from Figure 2 demonstrates a small task but plays a significant role in producing a feasible classification model. The data collection process begins with extensive cleansing and the transformation of raw data for subsequent machine learning use. The UE's data produce multiple attributes that transform high-dimensional data, and hence, a dimensional reduction algorithm from unsupervised learning is required before undergoing the clustering process.