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Abhijeet Goel

LIME Technique for Text Classification

LIME Technique for Text Classification

LIME stands for Local Interpretable Model agnostic Explanations. Local means related to a single prediction. Now, to the term model agnostic. It means that there is no need for information regarding the internal structure of the model.

Lime for Image Classification

Lime for Image Classification

You can not come to conclusion based only on numerical features. LIME helps to overcome this issue and gives you explanation about your AI model.

SHAP Feature Importance in Audio Classification

SHAP Feature Importance in Audio Classification

Audio Classification is one of the most widely used applications nowadays and a lot of research has been done on classifying audio using different kinds of features and neural network architectures. Some real-world applications of this is Speaker Recognition, Music Genre Classification, and Bird Sound Classification. The most commonly used

Guided Grad-CAM for White Box Explainability of Image Classifiers

Convolution Neural Network expanded its task from Image classification to Image captioning. With the expansion of this task, the architecture of CNN has been evolving into a more complex structure. With the increase in complexity, interpretability has been a major challenge. Because of these interpretability issues, should we trust CNN

Model Agnostic vs Model Specific Explainability

In recent past there is a trend of supplementing complex machine learning and deep learning algorithms with augmented explanations for the decisions taken by the complex ML models. This notion termed as XAI Explainable AI is a recent trend which has emerged to counter the criticism of complex AI models

Explain vs Interpret: What to choose?

In recent past one of the hot areas in AI has been the domain of Explainable AI also known as XAI. This initiative started by Darpa in US is aimed at making AI and decisions taken by AI systems more acceptable to humans. A key thought process underlying this is

Poka — Yoking your ML Model

Fail proofing your AI model -Dr Srinivas Padmanabhuni, CTO , testAIng.com A very popular notion in quality management is the notion of Poka-Yoke. It is invented in Japan for ensuring quality. Poka-Yoke means ‘mistake-proofing’ or more literally — avoiding (yokeru) inadvertent errors (poka). In context of our daily lives there are