Machine learning has been impacting a business revenue in a huge way, be it a top product-based company like Netflix where a recommondation system is worth $1 Billion, or any medium-scale company where machine learning models play a vital role in business decisions. By knowing its contribution towards revenue, should

Model explainability refers to the concept of being able to understand the machine learning model. Being able to interpret a model increases trust in AI. There are two ways to interpret a model i.e. Global vs Local Interpretation. Global interpretation helps us in understanding how a model makes decisions

Image Analysis is one of the most demanding tools to achieve automation in various sectors. Image Classification, Captioning, Detection, Segmentation, etc., are very crucial in various demanding technologies like self-driving cars. These image analysis tools are very crucial in today's HITECH world. Inside Image analysis software contains algorithms that execute

LIME stands for Local Interpretable Model Agnostic Explanations. Lime is one of the techniques of visualization that helps to explain individual predictions with model interpretability. Now, let's look what LIME means.. Local surrogate models are interpretable models used to explain individual predictions of black box models in machine learning. LIME

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 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.

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

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 a simple

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's queries.

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

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

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.

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.

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

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 comprehensive guide to what’