How To Compare Machine Learning Algorithms in Python with scikit-learn

Last Updated on August 28, 2020
It is important to compare the performance of multiple different machine learning algorithms consistently.
In this post you will discover how you can create a test harness to compare multiple different machine learning algorithms in Python with scikit-learn.
You can use this test harness as a template on your own machine learning problems and add more and different algorithms to compare.
Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.
- Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.

How To Compare Machine Learning Algorithms in Python with scikit-learn
Photo by Michael Knight, some rights reserved.
Choose The Best Machine Learning Model
How do you choose the best model for your problem?
When you work on a machine learning project, you often end up with multiple good models to choose from. Each model
To finish reading, please visit source site