Projects in Awesome Lists tagged with ensamble-methods

https://github.com/yuchenlin/llm-blender

[ACL2023] We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source LLMs. LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.

ensamble-methods large-language-models llm natural-language-processing

Last synced: 14 May 2025

https://github.com/yuchenlin/LLM-Blender

[ACL2023] We introduce LLM-Blender, an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source LLMs. LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.

ensamble-methods large-language-models llm natural-language-processing

Last synced: 29 Apr 2025

https://github.com/akarshankapoor7/adaboost_tutorial

The AdaBoost (Adaptive Boosting) algorithm is a popular ensemble method used in machine learning to improve the performance of weak classifiers. It combines multiple weak classifiers to create a strong classifier, focusing more on the misclassified instances in each subsequent iteration.

adaboostclassifier data-science ensamble-methods

Last synced: 18 Feb 2025

https://github.com/radhikareddy-chintareddy/predictive-churn-modeling-machine-learning-for-customer-retention

This project employs machine learning algorithms to predict customer churn by analyzing historical customer data. It provides actionable insights to enhance customer retention. The models were fine-tuned using hyperparameter optimization and tackled data imbalance with SMOTE, achieving high F1-scores to drive targeted business strategies.

ensamble-methods feature-selection hyperparameter-tuning machine-learning-algorithms python regression-algorithms smote-sampling

Last synced: 26 Mar 2025

https://github.com/joaopinto15/winapi_ml_model

This repository provides a complete infrastructure for the detection of malicious behavior in Windows environments through the monitoring and classification of WinAPI call sequences using supervised machine learning techniques. The goal is to identify patterns of execution typically associated with malware based on dynamic behavioral traces.

ensamble-methods machine-learning python random-forest winapi

Last synced: 13 May 2025

https://github.com/abhinav330/classification-problem-using-ensambling-on-titanic-dataset

a classification problem using ensemble methods on the Titanic dataset.

adaboost bagging bernoulli-naive-bayes ensamble-methods extratreesclassifier knn-classification random-forest-classifier svm-classifier titanic-kaggle

Last synced: 03 Mar 2025

https://github.com/marko19907/ml-assignments

Machine Learning assignments, Machine Learning (IE500618) course, fall 2022.

cifar100 confusion-matrix distributed-machine-learning ensamble-methods jyputer-notebook machine-learning mnist-dataset multilayer-perceptron-network mushroom-classification python resnet-50 transfer-learning

Last synced: 14 May 2025

https://github.com/jdtumlinson/decisiontrees_kmeans

This repository contains the code for the Decision Trees and k-Means Clustering assignment for CS434 Machine Learning and Data Mining at Oregon State University during Fall of 2024. This is being uploaded a few months afte the compleition of this assignment.

decision-trees ensamble-methods kmeans-clustering

Last synced: 01 Apr 2025