Nearest Shrunken Centroids With Python
Nearest Centroids is a linear classification machine learning algorithm.
It involves predicting a class label for new examples based on which class-based centroid the example is closest to from the training dataset.
The Nearest Shrunken Centroids algorithm is an extension that involves shifting class-based centroids toward the centroid of the entire training dataset and removing those input variables that are less useful at discriminating the classes.
As such, the Nearest Shrunken Centroids algorithm performs an automatic form of feature selection, making it appropriate for datasets with very large numbers of input variables.
In this tutorial, you will discover the Nearest Shrunken Centroids classification machine learning algorithm.
After completing this tutorial, you will know:
- The Nearest Shrunken Centroids is a simple linear machine learning algorithm for classification.
- How to fit, evaluate, and make predictions with the Nearest Shrunken Centroids model with Scikit-Learn.
- How to tune the hyperparameters of the Nearest Shrunken Centroids algorithm on a given dataset.
Let’s get started.
Nearest Shrunken Centroids With Python
Photo by Giuseppe Milo, some rights reserved.