You're reading from Machine Learning Engineering with MLflow

Product typeBook

Published inAug 2021

PublisherPackt

ISBN-139781800560796

Edition1st Edition

Tools

Maven

Concepts

Machine Learning

Author (1)

Natu Lauchande

Tuning your model with hyperparameter optimization

Machine learning models have many parameters that allow the developer to improve performance and control the model that they are using, providing leverage to better fit the data and production use cases. Hyperparameter optimization is the systematic and automated process of identifying the optimal parameters for your machine learning model and is critical for the successful deployment of such a system.

In the previous section, we identified the best family (in other words, LogisticRegression) model for our problem, so now it's time to identify the right parameters for our model with MLflow. You can follow along in the following notebook in the project repository, Chapter04/gradflow/notebooks/hyperopt_optimization_logistic_regression_mlflow.ipynb:

Importing dependencies: We will use the hyperopt library, which contains multiple algorithms to help us carry out model tuning:
```
from hyperopt import tpe
from hyperopt import...
```

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Machine Learning Engineering with MLflow

Published in: Aug 2021Publisher: PacktISBN-13: 9781800560796

Author (1)

Natu Lauchande

Natu Lauchande is a principal data engineer in the fintech space currently tackling problems at the intersection of machine learning, data engineering, and distributed systems. He has worked in diverse industries, including biomedical/pharma research, cloud, fintech, and e-commerce/mobile. Along the way, he had the opportunity to be granted a patent (as co-inventor) in distributed systems, publish in a top academic journal, and contribute to open source software. He has also been very active as a speaker at machine learning/tech conferences and meetups.
Read more about Natu Lauchande

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Tuning your model with hyperparameter optimization

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