13. Choosing the right estimator
13. Choosing the right estimator# Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems. The flowchart below is designed to give users a bit of a rough guide on how to approach problems with regard to which estimators to try on your data. Click on any est
1.7. Gaussian Processes
1.7. Gaussian Processes# Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of Gaussian processes are: The prediction interpolates the observations (at least for regular kernels). The prediction is probabilistic (Gaussian) so that one can compute empirical confidence intervals and decide based on
Nested versus non-nested cross-validation
Note Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder Nested versus non-nested cross-validation# This example compares non-nested and nested cross-validation strategies on a classifier of the iris data set. Nested cross-validation (CV) is often used to train a model in which hyperparameters also need to be optimized. Nested CV estima
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Getting started with Multivariate Adaptive Regression Splines – Depends on the definition
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