Further reading

Here are a few extra resources related to things we covered in this chapter:

• How to Choose an ML.NET Algorithm: https://learn.microsoft.com/en-us/dotnet/machine-learning/how-to-choose-an-ml-net-algorithm
• How to use the ML.NET Automated Machine Learning (AutoML) API: https://learn.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/how-to-use-the-automl-api
• Decision Trees vs Random Forests: https://www.statology.org/decision-tree-vs-random-forest/
• Classification algorithms in ML .NET: https://rubikscode.net/2021/02/01/machine-learning-with-ml-net-ultimate-guide-to-classification/
• Online Gradient Descent: https://deepgram.com/ai-glossary/online-gradient-descent
• LightGBM: https://github.com/microsoft/LightGBM
• Tweedie Distributions: https://en.wikipedia.org/wiki/Tweedie_distribution
• Ordinary Least Squares Regression: https://builtin.com/data-science/ols-regression
• Poisson Regression: https://en.wikipedia.org/wiki/Poisson_regression...