Optimization Techniques for Machine Learning Models to Improve the Efficiency of Classification

The objective of the study is to identify the most optimal set of hyperparameters for a machine learning (ML) or deep learning(DL) algorithms that improves its performance on a certain task.. This study uses five machine learning methods like Decision Tree(DT), Random Forest(RF), Gradient Boost Models(XGBoost), Support Vector Machine(SVMs) and K-Nearest Neighbhor(KNN). The model specific parameters were applied to all these ML methods to improve the accuracy of the models. The ML models performance with its hyperparameter tuning are evaluated for performance using the performance metrics like accuracy, precision, Recall and F-score. These findings indicate that XGBoost models performed significantly in terms of accuracy, precision, recall, and F1-score. Gradient boosting models are extremely adaptable, but they are also sensitive to hyperparameters such as the learning rate, number of estimators, and tree depth. Tuning these parameters can dramatically improve performance. The optimal model and tuning method are determined by the dataset, task specifications, and computing power.. The contribution of the study to suggests a suitable with right hyperparameter settings to develop a highly flexible model that can adapt to a variety of datasets. The study and application of model-specific hyperparameters in ML continues to evolve, resulting to advances that improve productivity, durability, and generalization.