# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 6 Discussion of the Results  
Each machine learning algorithm has its own pros and cons. For supervised techniques, the Random Forest algorithm presented the best results for both diabetes and human resource datasets as it uses the Ensemble Learning technique which does not require independency between attributes, and is more resilient to outliers and less impacted by noise [24].  
On the other hand, Logistic Regression showed lower results for some metrics, justified by the fact that no linearity exists between dependent and independent variables [20]. The same happened for the SVM Linear algorithm where the frontier is a straight line and there may be difficulty in separating distinct classes [21]. Naive Bayes is also an algorithm that does not show the best performance because it implicitly assumes that all the features are mutually independent, which is almost impossible to happen [23].  
In the diabetes dataset, we can mention that it is more important to minimize false negatives because the condition is not detected when it is present, and the _Recall_ metric, which involves the false negative, would be more important than the precision metric. In that sense, the Naive Bayes and Random Forest algorithms presented the best Recall values.  
For the human resource dataset, one can either privilege the reduction in false negatives, on retaining the best talents in the company, with the risk of retaining the worst employees, or privilege the reduction in false positives and having the worst employees leave the company with the risk of having a good employee leaving.  
The diabetes dataset had lower metric values compared to the human resource dataset, which can be justified by the fact that we need to do more value imputations to the features, because of the presence of invalid values. Additionally, because the diabetes dataset has fewer instances compared to the human resource dataset and lower feature correlation values, this results in poor performance.  
Generally, unsupervised techniques showed a lower performance in comparison with supervised techniques, because in the supervised technique, the algorithm learns from the training dataset by iteratively making predictions on the data and adjusting the correct answer. For unsupervised learning, in contrast, the algorithm works on their own to discover the inherent structure of unlabeled data. This is why supervised learning tends to be more accurate and have better performance results than unsupervised learning models.  
For Naive Bayes, the use of the preprocessing technique generally did not show improvements for the diabetes dataset or significant enhancements for the human resource dataset, because it is common knowledge that this algorithm is a bad estimator so the probability outputs are not to be taken too seriously. Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible that we obtain a set of predictors which are completely independent.  
The application of unsupervised clustering preprocessing techniques for the diabetes dataset did not result in better performance, while in the human resource dataset, we can clearly see that it had a positive impact on the performance. This fact is justified because the human resource dataset has more instances than the diabetes dataset, and it is common knowledge that too little training data results in poor approximation. Additionally, because the human resource dataset has higher feature correlation values than the diabetes dataset, it has better performance. This demonstrates that the pre-classification of the human resource dataset made by the preprocessing unsupervised clustering techniques positively impacts the performance results.