## 4 Results
### Evaluation of 27 Feature Selected Model (Proposed)  
In this subsection, the authors aim to holistically analyse the whole process of feature selection and the performance of the feature selection models using elimination time, accuracy and F1-score as evaluation metrics. This will largely help us to get an in-depth analysis of the whole process and will help us pinpoint the trade-offs and advantages of certain techniques over others.  
For building feature selection models, we performed recursive feature elimination using the inbuilt Scikit Learn API. Three Ensemble algorithms namely random forest, gradient boosting and AdaBoost were used to perform recursive feature elimination. The process resulted in three sets of features containing 60, 30 and 20 features, respectively. The respective feature sets were then trained using logistic regression, support vector machines and naive Bayes. The quantitative results of the time it took to perform feature selection are shown in Table 2; the accuracy of the selected features is shown in Table 3 and the F1-score is shown in Table 4.  
From Table 2, we can see that the elimination time of the random forest algorithm for finding the best possible set of features is minimum in the three cases where we aimed to find out the best 60, best 30 and best 20 features from our feature set of 121 features.  
Though the elimination time of the random forest algorithm might be the least when compared to the other two algorithms, that does not necessarily mean that the set of features the random forest (RF) was able to find in the three cases are the best for modelling our problem. Therefore, to reach conclusive evidence about which algorithm was able to find the best set of features for the cases we further needed to look into the performance of the machine learning algorithms which used those sets of features for testing and making predictions on the test data.  
From Tables 3 and 4, we can clearly see that AdaBoost (AB) was the best feature selection technique to identify the most valuable set of 60 features, having the highest accuracy and F1-score when used with a support vector machine as the classifier.  
Gradient boost (GB) as a feature selection technique was best able to select a set of 20 features to model the relationship between dependent and independent variables using a support vector machine.  
However, from Tables 3 and 4, it is evident that the combination of random forest for feature selection and support vector machine for training the classifier gave the highest accuracy and F1-score on the test data using only 30 features, when compared to other combinations of feature selection and training algorithms with varying number of features in the feature set. It can also be concluded from Table 4 that random forest has a comparatively lesser feature selection time compared to the other ensemble techniques hence making it a robust and time efficient technique.  
Figure 6: F1-score of baseline models.  
Furthermore, Tables 3 and 4 suggest that a set of 30 features is the optimal number of features for our particular problem as both increase to 60 features or decrease to 20 features caused a degradation in the accuracy and F1-score of our models.