Performance of various open source GBM implementations
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Updated
Jun 7, 2024 - HTML
Performance of various open source GBM implementations
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
This project involves developing a machine learning model to predict user preferences in chatbot conversations, using a dataset of head-to-head responses from various large language models. The goal is to enhance chatbot-human interactions by aligning chatbot responses more closely with human preferences.
Proyecto de Titulación: Cálculo de Pérdidas Esperadas basado en 3 modelos de Credit Scoring para una institución financiera del Ecuador
Exploring categorical features with various encodings and models
Scalable Python DS & ML, in an API compatible & lightning fast way.
Standardized Serverless ML Inference Platform on Kubernetes
Time series forecasting with scikit-learn models
A project to deploy an online app that predicts the win probability for each NBA game every day. Demonstrates end-to-end Machine Learning deployment.
Extreme Gradient Boosting (XGBoost) model for predicting hourly traffic volume. Utilized MAPE for model scoring, train-test splits with TimeSeriesSplit and hyperparameter tuning with GridSearchCV.
📘 The MLOps stack component for experiment tracking
Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
Distributed ML Training and Fine-Tuning on Kubernetes
The project aims to develop a machine learning model that predicts whether an employee will leave the company. A successful model will help identify key variables that drive employee turnover.
An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn
FLAMES is a tool for prioritizing genes in GWAS loci
Estudo transversal que analisou dados retrospectivos de gestantes e puérperas com diagnóstico de Síndrome Respiratória Aguda Grave (SRAG) entre janeiro de 2016 e novembro de 2021.
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