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Artificial Intelligence / Machine Learning

All the posts related to ai / ml learning

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

A Glass model with the herb features

Coefficients Based On Feature Importance in Text Classification

In this post, we will showcase one more Feature importance based on the Logistic regression model. If I say one more there must be an article previous to this i.e on shap feature importance if you have not gone through it here is the link. let's take

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

Figure : Source

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.

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.