ensemble-learning

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Here are 319 public repositories matching this topic...

pankajr141 / ensembler

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Powerful stacking/blending ensemble implementation in python.

ensemble ensemble-learning stacking blending

Updated Jun 7, 2016
Python

leftthomas / DREML

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A PyTorch implementation of DREML based on ECCV 2018 paper "Deep Randomized Ensembles for Metric Learning"

cnn pytorch ensemble-learning image-retrieval

Updated Jul 1, 2019
Python

being-aerys / Machine_Learning_CS534_Fall-2018

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Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost

machine-learning random-forest linear-regression ensemble-learning perceptron adaboost decision-tree average-perceptron polynomial-kernel

Updated May 18, 2019
Python

kristina100 / Machine_Learning

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🍀This is part of the work of the QG training summer camp.Machine learning based on Zhou Zhihua's watermelon book.

neural-network svm linear-regression machine-learning-algorithms pca ensemble-learning logistic-regression bayesian kmeans-clustering desisiontree

Updated Aug 5, 2021
Python

AnGoraGou / weight_seed_ensemble

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Implementation of ensemble method requires different models, to get different models it is better to have different pretrained model as initialising weight (seed weights ). In this repository a simple code has been implemented to generate such seed weights for ensembling.

ensemble-learning uncertainty-quantification uncertainty-estimation knowledge-distillation

Updated Aug 19, 2022
Python

i-l-sokolov / Simpsons_classification_with_ensembles_of_models

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Automation of model training and ensemble creation for making predictions in a Kaggle competition submission.

clustering ensemble-learning resnet greedy-algorithms torchvision efficientnet

Updated May 2, 2023
Python

ishmam-br10 / Machine-Learning-Models

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This codes are from a research project of mine that I conducted under the supervision of department of CSE , BRAC University

python machine-learning random-forest linear-regression regression naive-bayes-classifier ensemble-learning support-vector-machines knn-classification simple-linear-regression xgboost-classifier

Updated Aug 14, 2023
Python

abdulhakkeempa / spam-detection

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SMS Spam Classifier with FastAPI 🚀

python nlp ai naive-bayes sklearn pandas ensemble-learning spam-detection fastapi

Updated Oct 9, 2023
Python

sebseager / REPIC

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REliable PIcking by Consensus (REPIC)

graph-theory ensemble-learning cryo-em integer-linear-programming particle-picking

Updated Mar 24, 2024
Python

vadniks / akaBigData

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Technologies and tools for big data analysis

statistics clustering machine-learning-algorithms data-visualization python3 classification data-analysis ensemble-learning applied-mathematics association-rule-learning

Updated Dec 10, 2023
Python

DanRuta / Stanford-Cars-Deep-Learning

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Deep Learning experiments for the Stanford Cars dataset

machine-learning deep-learning pytorch neural-networks ensemble-learning tensorboard transfer-learning stanford-cars

Updated Mar 24, 2023
Python

jihyeonseong / SoM-TP

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Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings

time-series classification ensemble-learning pooling

Updated May 2, 2024
Python

spriyanth / imbalanced_classification

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Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"

ensemble-learning imbalanced-data hybrid-algorithms oversampling imbalanced-learning undersampling cost-sensitive-learning imbalanced-classification

Updated Jun 29, 2020
Python

AnchaNarasimha / Forecasting-and-Analysis-of-Cyber-Crimes

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This project employs ensemble learning methods to forecast cybercrime rates, utilizing datasets with population, internet subscriptions, and crime incidents. By analyzing trends and employing metrics like R2 Score and Mean Squared Error, it aims to enhance prediction accuracy and provide insights for effective prevention strategies.

random-forest machine-learning-algorithms xgboost ensemble-learning data-collection data-preprocessing boosting-algorithms feature-engineering comparative-analysis bagging

Updated May 18, 2024
Python

jaks19 / ensemble_gms

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Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, to solve inference tasks that were not seen during training, these models (EGMs) often need to be…

ensemble-learning graphical-models probabilistic-graphical-models adversarial-learning adversarial-machine-learning