Part of our mission is to identify #ML usability challenges. End-user comprehension is a major one. So we’ve developed a taxonomy to craft features non-#AI experts can understand & become more comfortable using #machinelearningmodel outputs. #ExplainableAI
🌟 #EditorsChoice🌟
In case you missed our previous post, we’d like to re-share the following article:
Review of Prediction Models for Chloride Ion Concentration in Concrete Structures, by Jiwei Ma et al.
🔗Read more for free at:
#machinelearningmodel
Webinar on " An Innovative Approach in Non-Parametric Machine Learning Model for Decision Making" organized by Department of Data Science, Institute of Artificial Intelligence, Saveetha School of Engineering, SIMATS on 13.09.2022
#SIMATS_Engg#Machinelearningmodel
Building a #MachineLearningModel to predict the probability of teams finishing in the top 8 of the #UCL! 🏆⚽
Leveraging team stats, opponent strength, and more.
Not a finished job (still need to improve the model performance) Excited to see how the predictions shape up!