Skip to content
#

susy

Here are 37 public repositories matching this topic...

Using the Sklearn classifiers: Naive Bayes, Random Forest, Adaboost, Gradient Boost, Logistic Regression and Decision Tree good success rates are observed in a very simple manner. In this work sensitivity is also considered. Treating each record individually, differences are found in the results for each record depending on the model used, which…

  • Updated Feb 9, 2022
  • Python

Improve this page

Add a description, image, and links to the susy topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the susy topic, visit your repo's landing page and select "manage topics."

Learn more