Topic: model-explainability Goto Github
Some thing interesting about model-explainability
Some thing interesting about model-explainability
model-explainability,Developed an efficient system to empower retailers with profitable insights & maintain a competitive edge in the dynamic retail industry.
User: ajay07pandey
model-explainability,Predict which powerlifters will have the highest one-rep-max deadlift
User: andrewdettor
model-explainability,Java client to interact with Arize API
Organization: arize-ai
model-explainability,A python library to send data to Arize AI!
Organization: arize-ai
model-explainability,Example projects for Arthur Model Monitoring Platform
Organization: arthur-ai
Home Page: https://arthur.ai
model-explainability,A curated list of awesome open source tools and commercial products for monitoring data quality, monitoring model performance, and profiling data ๐
Organization: awesome-mlops
model-explainability,Model explainability that works seamlessly with ๐ค transformers. Explain your transformers model in just 2 lines of code.
User: cdpierse
model-explainability,The Fraud Detection project aims to improve identification of fraudulent activities in e-commerce and banking by developing advanced machine learning models that analyze transaction data, employ feature engineering, and implement real-time monitoring for high accuracy fraud detection.
User: daniel-andarge
model-explainability,This project is a machine learning competition hosted on Kaggle platform, focused on forecasting Walmart's monthly and quarterly sales. We tasked with developing advanced predictive models to accurately predict Walmart's sales, taking into account various factors such as historical sales data, macroeconomic indicators, and local market conditions.
User: daniel-andarge
model-explainability,A proof-of-concept for the implementation of an early fault detection system in oil wells, designed to enhance operational efficiency and reduce costs.
User: gianatmaja
model-explainability,A reusable codebase for fast data science and machine learning experimentation, integrating various open-source tools to support automatic EDA, ML models experimentation and tracking, model inference, model explainability, bias, and data drift analysis.
User: gianatmaja
model-explainability,An application of the WhizML codebase for an analysis of cardiovascular disease risk.
User: gianatmaja
model-explainability,Writeup on classification model for predicting outcomes of NFL games, focusing on explainability. (+ project writeup)
User: jcguidry
model-explainability,Diffusers-Interpret ๐ค๐งจ๐ต๏ธโโ๏ธ: Model explainability for ๐ค Diffusers. Get explanations for your generated images.
User: joaolages
model-explainability,CrysXPP: An Explainable Property Predictor for Crystalline Materials (NPJ Computational Materials - 2022)
User: kdmsit
model-explainability,Capture fundamentals around ethics of AI, responsible AI from principle, process, standards, guidelines, ecosystem, regulation/risk standpoint.
User: kkm24132
model-explainability,Study on the performance of pre-trained models (VGG16, EfficientNetb0, ResNet50, ViT16) with weight fine tuning, as well as classical ML algorithms (Naive Bayes, Logistic Regression, Random Forest) on a dataset of 6.806 fungi microscopy Images utilizing Pytorch.
User: kyriakospsa
model-explainability,The official Python library for Openlayer, the Continuous Model Improvement Platform for AI. ๐
Organization: openlayer-ai
Home Page: https://www.openlayer.com
model-explainability,Machine Learning Final Project - December 04, 2021
User: pgplarosa
model-explainability,Machine Learning Individual Project - November 23, 2021
User: pgplarosa
model-explainability,This project implements an ML regression model for predicting cancer death rate in US.
User: popseli
model-explainability,This project develops an ML binary classification model to predict phishing webpages.
User: popseli
model-explainability,code for studying OpenAI's CLIP explainability
User: smamooler
model-explainability,This project provides a performance evaluation of credit card default prediction. Thus different models are used to test the variable in predicting the credit default and we found Random Forest Classifier performs the best with a recall of 0.95 on the test set.
User: syedsharin
model-explainability,Explaining Trees (LightGBM) with FastTreeShap (Shapley) and What if tool
User: zabir-nabil
Home Page: https://medium.com/@furcifer/explaining-trees-with-fasttreeshap-and-what-if-tool-dc0afde9613
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