Scikit-learn demo notebooks

24.4. Scikit-learn demo notebooks#

The Gaussian Process for Machine Learning page on the scikit-learn website is a great source of code and documentation and examples for GPs.

Here we have adapted their demonstration notebooks for:

  • Demonstration: One-dimensional Gaussian Process regression. Compares noise-free (interpolation) and noisy (regression) for a one-dimensional function (which can be easily changed). An RBF kernel is the default, but this is exchangeable for any of the standard sklearn kernels. A maximum likelihood fit determines the hyperparameters (so it might fail to find a good solution, but the hyperparameter values are given so this can be diagnosed).

  • Demonstration: prior and posterior Gaussian process for different kernels. This example illustrates the prior and posterior of the Scikit-learn class GaussianProcessRegressor with different kernels. Mean, standard deviation, and 5 samples are shown for both prior and posterior distributions.

We also have additional demo notebooks

  • Demo: Gaussian processes, which builds an RBF-based kernel (with signal scale and noise term), fits the GP on a subset (e.g., every 3rd point), predicts mean and uncertainty on a target grid or the full input, plots mean ±2σ and data, and computes simple validation metrics.

  • Exercise: Gaussian Processes, which build RBF kernels with signal variance and length-scale, fit GaussianProcessRegressor with a white-noise term, predict posterior mean and uncertainty, plot mean ±2σ and data, examine setting hyperparameters explicitly vs. optimizing by LML.

  • Gaussian Processes Exercises, which build RBF kernels and visualize samples, fit a GP to 1D data (train/test split), plot the posterior mean and ±2σ band, apply the workflow to a small dataset.