• AMAKU AMAKU
• Olumide Owolabi
• Agbogun B. Joshua
• Bamidele, Oluchi Jennie
• Igbinosa O. G

Random Forest models have been providing a notable performance on her predictive capacity to applications in the realm of behavioural-based Intrusion Detection Systems and other related fields of specialization which includes medicines, Banking, commerce, etc in terms high magnitude forecasting and optimal predictions . In this work, in-depth evaluation analysis of the Random Forest tuning are carried out with respect to classification, feature selection, and proximity metrics. This empirical research will provide an inclusive review of the general basic concepts related to Intrusion Detection Systems, which includes taxonomies, data collection, modeling and evaluation metrics. This work further remodels the Random Forest algorithm using RandomizedSearchCV method hyperparameter tuning as base-behavioral classifier to check and compare with its default in terms of efficiency in the realm of machine learning. NSL-KDD dataset were used for both training and testing of the tuned model using a supervised learning method. The predictive performance in the tuned model with respect to its matrix was higher, and comparison with other algorithms like Naïve bayes and Perception model, Ridge classifier proved that the RandomizedSearchCV hyperparameter tuning Random Forest algorithm performed more efficiently its results analysis and computation.

: RandomizedSearchCV, Hyperparameter, Decision Tree, Classifier, Random forest, Optimization, Tuning.

"OPTIMIZATION OF A HYBRID-BASED RANDOM FOREST ALGORITHM FOR NETWORK SYSTEMS USING RANDOMIZED SEARCH HYPERPARAMETER TUNING METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.e257-e314, January-2024, Available :http://www.jetir.org/papers/JETIRTHE2081.pdf

"OPTIMIZATION OF A HYBRID-BASED RANDOM FOREST ALGORITHM FOR NETWORK SYSTEMS USING RANDOMIZED SEARCH HYPERPARAMETER TUNING METHOD", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppe257-e314, January-2024, Available at : http://www.jetir.org/papers/JETIRTHE2081.pdf