However in recent years a field within machine learning called Explainable AI XAI has gained popularity which loosely translates to “explainable artificial intelligence”. What is the difference? Traditional models play the role of a black box into which after training it we put a set of data and expect some result. XAI deals with discovering the processes that take place in this black box making it transparent Glass Box . Thanks to this we are able to interpret the result and understand which data had what impact on it. InterpretML An innovation in this field is Microsoft's latest Python library InterpretML . Its algorithm is constructed in such a way that the final model generates easy to view and edit summary of the significance of individual features.
Thanks to this after training it we get a full overview of them and additionally during prediction we find out which features influenced the model's decision. business analytics Summary of the EBM binary classifier prediction result from the InterpretML library Thanks to such a tool we can get very valuable answers about our business by asking the Taiwan WhatsApp Number List right questions. Business analytics what do you need? Machine learning models significantly extend the possibilities of BI but to fully use them you must additionally have development resources hardware resources. Development resources can be your own if you have the knowledge.
Otherwise you need to resort to the help of specialists. Hardware resources depend primarily on the data you want to work with. Fortunately nowadays there are many ways to access them where you only pay for consumption. For simple datasets even the free version of Google Collab which supports the creation of notebooks in Python will work great. Analysis of sales value using models a case study In order not to rely only on theoretical aspects we will use a publicly available dataset for a coffee tea and accessories store.