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Abhijeet Goel

The Importance of PDP in Regression Problems

The Importance of PDP in Regression Problems

Model interpretability is becoming a progressively appreciated aspect. Though, when handling large models with complex relationships, interpretation is always a tedious task but is a very important part. PDP is a part of Interpretability which can be put on Supervised and Unsupervised Learning problems. Partial dependence plots show how each

SHAP Instance Level Importance for Text Classification

SHAP Instance Level Importance for Text Classification

SHAP stands for SHapley Additive exPlanations. It uses Shapley values as its core to explain individual predictions. Shapley values come from Game Theory where each feature present in the data is the player in the game and the final reward is the prediction that is made.

Model Explainability using SHAP for Regression Problems

Model Explainability using SHAP for Regression Problems

Nowadays we are able to solve various problems using AI. We have built a model that can predict the price of a house in an area, a model which can predict the team that is most likely to win the IPL using data, and predict who will qualify for NFL

Testing a Chatbot ! How

Overview of ChatBot Testing

What is chatbot? A software or computer program that simulates human conversation. Chatbot testing is necessary to make sure that an effective and efficient chatbot is deployed. Testing experts analyze if all the necessary features are implemented correctly or not, and make sure they can appropriately answer each user'

SHAP Feature Importance in Structured Classification

SHAP Feature Importance in Structured Classification

Recently the European Union (EU) through the High-Level Expert Group on Artificial Intelligence (AI-HLEG) and the General Data Protection Regulation (GDPR) has recently posed an interesting challenge to the AI community, by demanding a more user-centered approach to explaining Automated Decision-Making systems (ADMs) using explainable AI(XAI) XAI is a

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Partial Dependence Plots in Structured Classification

Interpreting machine learning models can be defined as a process of identifying the different characteristics of that particular model. The main reason for interpreting/explaining the machine learning model is that the user wants to know how the model works and has trust issues. There are several interpretability techniques, which

Grad-CAM++: White Box Explainability for Image Classification

Grad-CAM++: White Box Explainability for Image Classification

Grad-CAM ++ is one of the most reliable and widely used explainability techniques for CNN because of its lucidity and efficaciousness.