International Journal of Science, Engineering and TechnologyAuthors: Sudhanshu, Nitin Gupta, Dev Button, Jyoti Bansal, Vinayak Sharma
Abstract: Machine learning models usually work better or worse depending on how well they can use what they learned from the training data to make predictions about new data. Overfitting and underfitting are the two biggest problems in this area. Both can have a big impact on how reliable and accurate the model is at making predictions. This is a meta-analysis of overfitting, underfitting, and generalization that looks at a lot of different machine learning models and datasets. The meta-analysis integrates theoretical frameworks and empirical experiments to examine the training and testing performances across various model complexities. The generalization gap is the main way to measure how well a model works, and the bias-variance tradeoff is the main idea behind the whole paper. The research studies used standard benchmark datasets and a range of algorithms, such as linear models, decision trees, ensemble methods, and neural networks. Error curves, comparative metrics, and statistical summaries were all used to show the patterns in model performance.
DOI: http://doi.org/10.5281/zenodo.21768178
