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What is Hyperparameter Tuning

Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
This is a process of fine-tuning the hyperparameters to achieve optimal model performance. This process typically involves randomized-search or grid-search methods applied in 5-fold or 10-fold cross-validation regions.
Published in Chapter:
Applying Machine Learning Methods for Credit Card Payment Default Prediction With Cost Savings
Siddharth Vinod Jain (Liverpool John Moores University, UK) and Manoj Jayabalan (Liverpool John Moores University, UK)
DOI: 10.4018/978-1-7998-8455-2.ch011
Abstract
The credit card has been one of the most successful and prevalent financial services being widely used across the globe. However, with the upsurge in credit card holders, banks are facing a challenge from equally increasing payment default cases causing substantial financial damage. This necessitates the importance of sound and effective credit risk management in the banking and financial services industry. Machine learning models are being employed by the industry at a large scale to effectively manage this credit risk. This chapter presents the application of the various machine learning methods like time series models and deep learning models experimented in predicting the credit card payment defaults along with identification of the significant features and the most effective evaluation criteria. This chapter also discusses the challenges and future considerations in predicting credit card payment defaults. The importance of factoring in a cost function to associate with misclassification by the models is also given.
Full Text Chapter Download: US $37.50 Add to Cart
More Results
Big Data Application of Breast Cancer Prediction: A Healthcare 5.0 Application for Smart Cities
Hyperparameter tuning is a critical process in machine learning that involves selecting the optimal values for the hyperparameters of a model. Hyperparameters are parameters that are set before the learning process begins and directly influence the model's performance and behavior.
Full Text Chapter Download: US $37.50 Add to Cart
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