Powerful stacking/blending ensemble implementation in python.
-
Updated
Jun 7, 2016 - Python
Powerful stacking/blending ensemble implementation in python.
A PyTorch implementation of DREML based on ECCV 2018 paper "Deep Randomized Ensembles for Metric Learning"
Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost
🍀This is part of the work of the QG training summer camp.Machine learning based on Zhou Zhihua's watermelon book.
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.
Automation of model training and ensemble creation for making predictions in a Kaggle competition submission.
This codes are from a research project of mine that I conducted under the supervision of department of CSE , BRAC University
SMS Spam Classifier with FastAPI 🚀
REliable PIcking by Consensus (REPIC)
Technologies and tools for big data analysis
Deep Learning experiments for the Stanford Cars dataset
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings
Supplementary codes of the Master Thesis "Binary Classification on Imbalanced Datasets"
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.
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 Methods -- Stacking using Python
Visual-analytical tools to evaluate and compare the outputs of large numbers of binary classifiers.
Tool for creating multi-output deep ensemble neural-networks
Repository for Reproducibility for the Paper: "Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML"
PAN 2019, Bots and Gender Profiling Task
Add a description, image, and links to the ensemble-learning topic page so that developers can more easily learn about it.
To associate your repository with the ensemble-learning topic, visit your repo's landing page and select "manage topics."