Abstract
Model performance is a broad term generally used to measure how the model performs on a new dataset, usually a test dataset. The performance metrics also play the role of thresholds to decide whether the model can be put into actual decision making systems or needs improvements. In the previous chapter, we discussed some performance metrics for our continuous and discrete cases. In this chapter, we will discuss how changing the modeling process can help us improve model performance on the metrics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 Karthik Ramasubramanian and Abhishek Singh
About this chapter
Cite this chapter
Ramasubramanian, K., Singh, A. (2017). Model Performance Improvement. In: Machine Learning Using R. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-2334-5_8
Download citation
DOI: https://doi.org/10.1007/978-1-4842-2334-5_8
Published:
Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-2333-8
Online ISBN: 978-1-4842-2334-5
eBook Packages: Professional and Applied ComputingProfessional and Applied Computing (R0)Apress Access Books