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Shapash 1.3.2, announcing new features for more auditable AI


Shapash is a Python library released by MAIF data team in January 2021 to make Machine Learning models understandable by everyone. Shapash is currently using a Shap backend to compute local contributions. You will find the general presentation of Shapash in this article. Version 1.3.2 is now available and Shapash allows the Data Scientist to document each model he releases into production. Within a few lines of code, he can include in an HTML report all the information about his model (and its associated performance), the data he uses, his learning strategy, … this report is designed to be easily shared with a Data Protection Officer, an internal audit department, a risk control department, a compliance department or anyone who wants to understand his work.

Shapash- Python Library To Make Machine Learning Interpretable


The above quote is quite interesting and yes, they speak the truth most of us are from the technical field so we probably know about what machine learning is? it is the current worldwide digital technology ruled over the world. If you are familiar with machine learning then you come across the words data, train, test, accuracy, and many more, and many of you are capable of writing machine learning scripts if you notice that we didn't see the background calculations of the machine learning models because machine learning is not interpretable. Many people say that the machine learning models are the black box models, suppose if we give input there are a lot of calculations are happening inside and we got the output, that particular calculation based on what feature we are actually giving. Suppose we give the input of 5 features inside this, it may be a situation where some of the feature value may be increasing and some of them are decreasing, so we not able to see this, but python has a beautiful library which makes a machine learning model interpretable by this we can able to understand that underground calculations. This beautiful library is developed by a group of MAIF Data Scientists.

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