What are the different calibration methods and how do they compare?

What are the different calibration methods used in machine learning?

There are several calibration methods, including Platt Scaling, Isotonic Regression, and Beta Calibration. Each has its strengths and weaknesses.

Can you explain how Platt Scaling works?

Platt Scaling uses a logistic regression model to transform the output of a classifier into calibrated probabilities. It fits a sigmoid function to the scores.

What about Isotonic Regression? How is it different?

Isotonic Regression is a non-parametric method that fits a piecewise constant function to the data. It's more flexible than Platt Scaling but requires more data.

Which method is generally preferred?

The choice depends on the dataset size and distribution. Platt Scaling is often preferred for smaller datasets, while Isotonic Regression works better with larger datasets.