To understand housing prices better, simplicity and clarity in our models are key. Our aim with this post is to demonstrate how straightforward yet powerful techniques in feature selection and engineering can lead to creating an effective, simple linear regression model. Working with the Ames dataset, we use a Sequential Feature Selector (SFS) to identify the most impactful numeric features and then enhance our model’s accuracy through thoughtful feature engineering. Let’s get started. The Strategic Use of Sequential Feature Selector […]

Image by Author In this tutorial, we will explore Retrieval-Augmented Generation (RAG) and the LlamaIndex AI framework. We will learn how to use LlamaIndex to build a RAG-based application for Q&A over the private documents and enhance the application by incorporating a memory buffer. This will enable the LLM to generate the response using the context from both the document and previous interactions. What is RAG in LLMs? Retrieval-Augmented Generation (RAG) is an advanced methodology designed to enhance the performance […]

Many beginners will initially rely on the train-test method to evaluate their models. This method is straightforward and seems to give a clear indication of how well a model performs on unseen data. However, this approach can often lead to an incomplete understanding of a model’s capabilities. In this blog, we’ll discuss why it’s important to go beyond the basic train-test split and how cross-validation can offer a more thorough evaluation of model performance. Join us as we guide you […]