Performance Accuracy and Matthew Correlation Coefficient
The models were analyzed using the SK learn library after dropping the
features with p-values greater than 0.05. Table 1 presents the
performance accuracy and the MCC results for the different models
tested. Compared to the gradient descent and the ensemble classifier,
the benchmark logistic regression model did not fear much. The ensemble
classifier, a combination of multiple models, showed the best
performance in terms of accuracy and MCC. The superior performance of
the ensemble classifiers is because they can capture different patterns
in the data and weigh them appropriately, resulting in more accurate
predictions 41,56. In terms of computational cost,
gradient descent was the most expensive model to run, while logistic
regression was the least expensive 34. Therefore, if
computational cost is a concern, logistic regression may be a better
option despite its slightly lower accuracy. Overall, all the models
performed reasonably well, with ensembles being the best performers in
terms of accuracy and MCC.
As shown in Table 6, the MCC had poorer performance overall than the
model accuracy. The random forest model had the highest performance
accuracy (.89) and MCC (.78) on the test set, although it showed signs
of overfitting. The next best performing classifier was the decision
tree classifier, with a performance accuracy of .86 and an MCC of .75.
The gradient descent classifier had a similar accuracy (.82); but a
lower MCC (.67). These results suggest that while the random forest
model is more likely to overfit the data, it may still be the best
option for this dataset due to its higher accuracy. However, further
tuning of hyperparameters may be necessary to improve its performance.
These results suggest that, while gradient descent may be a
computationally efficient method, it is not as effective as other
methods in predictive power. In conclusion, the random forest model is
the best-performing model in terms of accuracy and MCC.
Table 6: Algorithms Performance