𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁𝘀 𝗼𝗳 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝘁𝘂𝗮𝗹𝘀

𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁𝘀 𝗼𝗳 𝗖𝗼𝘂𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝘁𝘂𝗮𝗹𝘀

Counterfactual explanations show you how to change an input to get a different result. Most methods rely on finding the closest possible point to your original data.

Gradients of counterfactuals change this approach. This method uses gradient information to guide the search for new data points.

Why this matters:

• It improves the quality of the explanations.
• It helps models provide more useful feedback.
• It makes the process faster and more accurate.

You use these gradients to find the smallest changes needed to flip a model decision. This provides clarity for users and developers.

Source: https://dev.to/paperium/gradients-of-counterfactuals-2f6o

Optional learning community: https://t.me/GyaanSetuAi