# Improving Automated Machine-Learning Systems through Green AI
## 6 Discussion  
The results of the analysis provide valuable insights into the performance and energy efficiency of different classification models and AutoML optimization techniques for a multiclass problem. A trade-off emerges between predictive performance and energy efficiency in machine-learning models, where the more complex models like Gradient Boosting, Random Forest, and Support Vector Machines achieve remarkable accuracy and F1 scores but also exhibit significantly higher energy consumption and CO\({}_{2}\)e emissions. In contrast, simpler models like Decision Trees, Naive Bayes, and Linear SVM offer lower emissions but come at the cost of diminished predictive performance. Specifically, the complex models attain 2-5% higher precision in metrics such as accuracy and F1 yet simultaneously demonstrate a 5 to 10-fold surge in energy consumption and emissions. For example, while Random Forest achieves an impressive accuracy of 0.94, it emits 0.014 kg of CO\({}_{2}\)e, whereas Decision Trees, with an accuracy of 0.92, emit a mere 0.0001 kg of CO\({}_{2}\)e. In relative terms, Decision Trees offer a 2% reduction in accuracy while achieving a staggering 99% reduction in CO\({}_{2}\)e emissions compared to Random Forest, illustrating the inherent trade-off between predictive performance and energy efficiency/emissions. Thus, simpler models like Decision Trees and Naive Bayes, though green in their minimal processing requirements, involve some sacrifice in precision, aligning with established research confirming the link between heightened model complexity and elevated computational costs [25]. Figure 4: The figure shows the results of the calculation of the means, medians, standard deviations, and interquartile ranges (IQR) for both samples. It also shows the result of the Mann–Whitney U statistical test, which determined if there are significant differences between the samples. In Bayesian optimization, the primary hyperparameters under consideration included the number of estimators in Random Forest (ranging from 100 to 500 trees), the maximum tree depth in Decision Trees (varying from 5 to 20 levels), and the number of boosting iterations in Gradient Boosting (ranging from 50 to 200). The selection of these search ranges was guided by recommendations from the existing literature. Conversely, in a random search, these same hyperparameters were randomly sampled. Bayesian search notably enhanced the balanced accuracy for Gradient Boosting (0.9309 vs. 0.9256) by identifying near-optimal values around 150 boosting iterations. Nonetheless, in a broader context, both approaches yielded similar performance metrics for the primary models. For instance, with Random Forest, Bayesian search achieved an accuracy of 0.9414 using 300 estimators, while random search reached 0.9415 with 250 estimators. A comprehensive exploration of the subtle differences in optimization performance would necessitate further in-depth investigations into hyperparameter responses and the search space. By meticulously examining specific hyperparameters and search ranges, we gain valuable technical insights into the AutoML optimization process. Although both Bayesian and random search methods demonstrated comparable metrics for the leading models, a more exhaustive analysis of the minor discrepancies could reveal the relative strengths of each method. Both methods achieved favorable outcomes in AutoML optimization, with similar performance metrics under both techniques. Bayesian optimization held a slight advantage in fine-tuning particular models like Gradient Boosting, but the differences were marginal, confirming the feasibility of both methods as AutoML optimizers. This finding aligns with prior research, which consistently highlights the similar performance levels achieved by these hyperparameter tuning methodologies [38].