l1l2py is a Python package to perform variable selection by means of l1l2 regularization with double optimization.
-
Updated
Dec 5, 2017 - Python
#
l1l2-regularization
Here are 4 public repositories matching this topic...
This project leverages advanced machine learning algorithms to detect and classify malicious emails, focusing on spam and phishing threats. As email threats grow more sophisticated, accurate detection is critical to ensuring the security and privacy of both individuals and organizations.
nlp flask machine-learning pipeline cross-validation pytorch transformer nltk ensemble-learning spam-detection bert l1l2-regularization outlook-addin noise-injection pythonanywhere stacked-ensembles phishing-detection optuna
-
Updated
Dec 9, 2024 - Python
A machine learning repository for a variety of algorithms (KNN, Naive Bayes etc)
random-forest svm logistic-regression decision-trees knn naive-bayes-algorithm l1l2-regularization smote xgboost-algorithm machienlearning
-
Updated
Nov 16, 2023 - Jupyter Notebook
Machine Learning algorithms
polynomial-regression decision-tree l1l2-regularization gradient-boosting kmeans-algorithm dbscan-algorithm
-
Updated
Apr 24, 2024 - Jupyter Notebook
Improve this page
Add a description, image, and links to the l1l2-regularization topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the l1l2-regularization topic, visit your repo's landing page and select "manage topics."