Interpret a Black-Box Neural Network Model with a No-Code Platform

Most of the ML algorithms or NN models are notorious as a black box, as it’s difficult to interpret the predictions or cause of predictions. Model Interpretation enables the data scientist to generate insights from the trained model and explain the model outcomes to the stakeholders.

There are many open-source libraries/frameworks that provide model interpretability, such as SHAP, LIME, ELI5, and others. It is difficult for clients or business people to apply the above-mentioned packages to understand their models since the deployment requires some programming and data science experience. In this article, we will look at Neuton, an AutoML no-code platform.

In only a few clicks, AI can provide actionable insights and explainability of neural network models trained on any bespoke dataset.

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