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Evaluating and Optimizing Artificial Intelligence Models

In: Managing Artificial Intelligence

Author

Listed:
  • Nils Urbach

    (Frankfurt University of Applied Sciences)

  • Daniel Feulner

    (University of Bayreuth)

  • Tobias Guggenberger

    (University of Bayreuth)

  • Annalena Schmid

    (University of Hohenheim)

Abstract

In this chapter, the initial focus is on how AI models fit into a broader hierarchy, distinguishing them from foundational mathematics and algorithms. This is followed by a discussion of the key challenges in AI model development and the central role of metrics in addressing them. Next, various metrics are presented in detail, along with hyperparameter optimization methods aimed at improving model performance. The final section highlights the characteristics of a well-trained AI model, emphasizing strategies to avoid both underfitting and overfitting for robust, reliable results.

Suggested Citation

Handle: RePEc:spr:fuobcp:978-3-032-13308-3_5
DOI: 10.1007/978-3-032-13308-3_5
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