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.
Artificial Intelligence / Machine Learning
All the posts related to ai / ml learning
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
SHAP Feature Importance in Text Classification
In this blog, we'll be primarily focused on the text classification task of Natural language processing (NLP). We'll be using quality entertainment resources called Rotten Tomatoes and the Tomato meter score online aggregator of movie and TV show reviews from critics, which provide fans with a
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