Unveiling Machine Learning: The PiML Toolbox for Enhanced Explainability

Author(s): Himanshu Sharma

Originally published on Towards AI.

Demystifying Complex Models with Transparency and Interpretability

PiML(Source: By Author)

Python modules like sklearn, lazy predict, etc., have made it simple to develop a machine-learning model. These libraries may be quickly learned and put to use in order to develop models, visualize those models, and evaluate how well they work.

The fundamental problem nowadays is that models are not readily understood, making it hard for a layperson to grasp the model’s reasoning and inner workings.

The rising complexity of machine learning models has made it harder to interpret their results and justify their choices. To guarantee openness, credibility, and legal conformity, however, explainability is essential. The PiML Toolbox is an… Read the full blog for free on Medium.

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Published via Towards AI