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Machine Learning in Biotechnology and Life Sciences

You're reading from   Machine Learning in Biotechnology and Life Sciences Build machine learning models using Python and deploy them on the cloud

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801811910
Length 408 pages
Edition 1st Edition
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Author (1):
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Saleh Alkhalifa Saleh Alkhalifa
Author Profile Icon Saleh Alkhalifa
Saleh Alkhalifa
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Data
2. Chapter 1: Introducing Machine Learning for Biotechnology FREE CHAPTER 3. Chapter 2: Introducing Python and the Command Line 4. Chapter 3: Getting Started with SQL and Relational Databases 5. Chapter 4: Visualizing Data with Python 6. Section 2: Developing and Training Models
7. Chapter 5: Understanding Machine Learning 8. Chapter 6: Unsupervised Machine Learning 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Understanding Deep Learning 11. Chapter 9: Natural Language Processing 12. Chapter 10: Exploring Time Series Analysis 13. Section 3: Deploying Models to Users
14. Chapter 11: Deploying Models with Flask Applications 15. Chapter 12: Deploying Applications to the Cloud 16. Other Books You May Enjoy

Overfitting and underfitting

Within the context of SML, we will prepare our models by fitting them with historical data. The process of fitting a model generally outputs a measure of how well the model generalizes to data that is similar to the data on which the model was trained. Using this output, usually in the form of precision, accuracy, and recall, we can determine whether the method we implemented or the parameters we changed had a positive impact on our model. If we revisit the definition of ML models that from earlier in this chapter, we specifically refer to them as models that learn or generalize from historical data. Models that are able to learn from historical data are referred to as well-fitted models, in the sense that they are able to perform accurately on new and unseen data.

There are instances in which models are underfitted. Underfitted models generally perform poorly on datasets, which means they have not learned to generalize well. These cases are generally...

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