AI Explainability(XAI) and XAI python methods

In brief Explainable artificial intelligence (XAI) is the process of making the outcome of non-linearly programmed systems more transparent, in an effort to avoid “black-box” processes. XAI in Computer Science is defined by tools and frameworks to help in understanding and interpret predictions made by the various machine learning models. Libraries The most commonly used…

Generalizing Machine Learning Pipelines: From Data Balancing to Model Evaluation

Implementation A generalized python code to be used for classification tasks. Sklearn, tensorflow and keras are the main libraries used. For inference examples use the following github link Usage • Data Loading: You can load data from any SQLite database or CSV file by specifying the path and, if necessary, the SQL query. • Preprocessing…