## 6 Discussion  
The utilization of Gradient Boosting in this research has notable benefits, including its resilience to outliers, ability to handle diverse data formats, and provision of valuable insights into the value of features. These traits are particularly well suited to the characteristics of academic performance data. Nevertheless, it is crucial to acknowledge the possible drawbacks associated with this approach. One potential limitation is the susceptibility to overfitting if the method is not adequately fine-tuned. Additionally, the computational requirements may increase significantly, particularly when dealing with a large number of trees. Furthermore, the success of the method might be very dependent on the individual  
Figure 7: ML models’ outcome using RFE selected features.  
hyperparameters that are selected. These factors are essential for analyzing the effectiveness and suitability of the strategy in forecasting student outcomes.  
_Risks and Considerations in Applying Machine Learning to Educational Analytics_  
The utilization of machine learning presents a potentially fruitful route for comprehending and forecasting student performance. However, it is imperative to adopt a nuanced perspective when interpreting the outcomes it produces. An inherent risk associated with a heavy reliance on prediction models is the possibility of oversimplifying complex phenomena. By placing excessive emphasis on mean values and prevalent trends, there is a risk of unintentionally disregarding exceptional cases or aberrations. These differences, rather than being simply oddities, may potentially signify distinct learning paths or particular obstacles encountered by specific student populations. Moreover, an excessive focus on predictive analytics may accidentally result in educators giving priority to conforming to the model, which could potentially marginalize the wider educational objectives of promoting critical thinking, creativity, and comprehensive growth. The effectiveness of machine learning is highly dependent on its careful implementation, similar to any other instrument. The role of technology in education should be seen as an addition to, rather than a replacement for, the intuitive and experiential knowledge possessed by educators.  
Numerous scholars have previously underscored the possible drawbacks associated with the convergence of machine learning and the field of education. A variety of challenges can be identified, encompassing ethical considerations such as the possibility of algorithmic prejudice, as well as educational matters that involve an excessive dependence on quantitative measurements to the detriment of qualitative perspectives. This study emphasizes the utilization of data analytics while also advocating for a comprehensive approach that combines algorithmic precision with the profound and multifaceted insights derived from traditional educational methods.  
The values of MAE and RMSE are very close in our study, so it might be beneficial to include additional criteria to determine the best model. Some factors to be considered are as follows:  
* Computational efficiency: if some models are substantially faster to train or require less memory, this could be a deciding factor.
* Model complexity: simpler models are generally preferable if performance metrics are very close, as they are easier to interpret and less likely to overfit.
* Domain-specific criteria: are there specific requirements in the educational context that might make one type of error (e.g., false positives vs. false negatives) more critical than another?
* Statistical significance: conducting a statistical test to check whether the differences in MAE and RMSE are statistically significant could provide a more definitive answer. If none of these additional criteria provide a clear winner, it is scientifically acceptable to state that the models perform comparably based on the metrics used.