Improving the Performance of Machine Learning with Sequential Feature Selection and Grid Search

Feature selection is an important step in developing accurate machine -learning models for classification tasks, including wine quality prediction. The accuracy of the machine learning model depends on the selection of relevant features that contribute to the predicted outcome. In this paper, we propose two commonly used optimization methods, forward sequential feature selection (SFS), and grid search, to identify the most relevant features for wine quality prediction using K -nearest neighbor (KNN). We used a dataset of 1598 samples with 11 wine -quality features and evaluated the performance of the KNN model trained on different subsets of features selected SFS. The result suggests that SFS and gird search are effective methods for wine quality prediction using KNN. The identified wine quality features help to predict the quality of wine more accurately, leading to better predictive outcomes. Thus, machine learning models can benefit greatly from the use of grid search and SFS. By fine-tuning the model in this way, it is possible to achieve better results in applications where accuracy and speed are important. As machine learning continues to be used in a wide range of industries, the use of these techniques will become increasingly important. Further research is needed to validate the model on larger datasets and to integrate it into practical classification or predictive analysis.