Explainable machine learning at your fingertips.Black-box models aren’t cool anymore. It’s easy to build great models nowadays, but what’s going on inside? That’s what Explainable AI and LIME try to uncover.
Interpreting and Explaining the predictions made by Machine Learning Models using LIME. What if I tell you to invest $100,000 in a particular stock today as my machine learning model is predicting a high return.
Ultimate Guide to Model Explainability: Anchors. There is now a laundry list of Machine Learning and Deep Learning algorithms to solve each AI problem.
Train, test and explain a diabetes classification model in Python. In the supervised machine learning world, there are two types of algorithmic task often performed. One is called regression (predicting continuous values) and the other is called classification (predicting discrete values). Black box algorithms such as SVM, random forest, boosted trees, neural networks provide better prediction accuracy than conventional algorithms.
Opening black-box models with LIME — Beauty and the beast: A simple step-by-step guide (with Python codes) that truly explains what LIME is and how it works as well as some potential pitfalls.