What is local interpretable model-agnostic explanations?

What is local interpretable model-agnostic explanations?

Local Interpretable Model-agnostic Explanation (LIME) is a recent technique that explains the predictions of any classifier faithfully by learning an interpretable model locally around the prediction. However, the sampling operation in the standard implementation of LIME is defective.

What is lime explainer?

What is LIME? LIME ( Local Interpretable Model-agnostic Explanations )is a novel explanation technique that explains the prediction of any classifier in an interpretable and faithful manner by learning an interpretable model locally around the prediction.

How do I make someone trust me again?

Rebuilding trust when you’ve hurt someone

1. Consider why you did it. Before you embark on the process of rebuilding trust, you’ll first want to check in with yourself to understand why you did it.
2. Apologize sincerely.
3. Give your partner time.
4. Let their needs guide you.
5. Commit to clear communication.

What is protocol agnostic?

Protocol-agnostic software is independent of communication protocols. It negotiates a protocol with its peer and begins communication. Vendor-agnostic middleware can mediate between software from multiple vendors, rather than between two specific applications.

Which is local interpretable model agnostic explanation in lime?

This is an R port of the Python lime package (https://github.com/marcotcr/lime) developed by the authors of the lime (Local Interpretable Model-agnostic Explanations) approach for black-box model explanations. All credits for the invention of the approach goes to the original developers.

Which is local interpretable model-agnostic explanation in Python?

This is an R port of the Python lime package ( https://github.com/marcotcr/lime) developed by the authors of the lime (Local Interpretable Model-agnostic Explanations) approach for black-box model explanations. All credits for the invention of the approach goes to the original developers.

How to express local surrogate models with interpretability constraint?

Mathematically, local surrogate models with interpretability constraint can be expressed as follows:

How to train models with exactly k features?

There are several methods for training models with exactly K features. A good choice is Lasso. A Lasso model with a high regularization parameter λ λ yields a model without any feature. By retraining the Lasso models with slowly decreasing λ λ, one after the other, the features get weight estimates that differ from zero.