IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification

Optimising hyper-parameters in Random Forest is a time-consuming undertaking for several academics as well as professionals. To acquire greater performance hyper-parameters, specialists should explicitly customize a series of hyper-parameter settings. The best outcomes from this manual setting are then modelled and implemented in a random forest algorithm. Several datasets, on the other side, need various prototypes or hyper-parameter combinations, which may be time-consuming. To solve this, we offered various machine learning models and classifiers for correctly optimising hyper-parameters. Both genetic algorithm-based random forest and randomised CV random forest were assessed on performance measures such as sensitivity, accuracy, specificity, and F1-score. Finally, when compared to randomised CV random forest, our suggested model genetic algorithm-based random forest delivers more incredible accuracy.