Tag: ML

Hi there! In this post, we’ll explore CatBoost in depth — what it is, why it was created, how it works internally (including symmetric trees, ordered boosting, and ordered target statistics), and guidance on when to use or avoid it. 🐈 What is CatBoost? CatBoost is a gradient boosting library…

Decision Trees are one of the most intuitive and interpretable models in machine learning. They are widely used in both classification and regression problems due to their simplicity and flexibility. Below, we cover their internal workings, strengths, limitations, and answer key interview questions. 🌳 What Is a Decision Tree? A…

In our last blog, we talked about feature engineering, and hopefully, you got excited and created dozens — if not hundreds — of new features. Now, you may be wondering: Which ones should I actually use in my model? Don’t worry — we’ve all been there. Welcome to the world…

As we continue our journey through machine learning model development, it’s time to shine a light on one of the most critical yet underrated aspects — Feature Engineering. If you ever wondered why two people using the same dataset and algorithm get wildly different results, the answer often lies in…

Great job sticking through the foundational parts of ML so far. Now let’s talk about something crucial — how to handle categorical variables. This is one of the first real technical steps when working with data, and it can make or break your model’s performance. 🧠 Why Do We Need…

Let’s talk about a topic that often gets underestimated — classification metrics in machine learning. I know many of you are eager to dive into LLMs and the shiny new world of GenAI. But here’s the truth: without building a strong foundation in traditional ML, your understanding of advanced systems…

Understanding the Right Way to Measure Accuracy In machine learning, building a regression model is only half the work. The other half—and just as important—is evaluating its performance. But how do we know if the model is good? And how do we convince business stakeholders that it works? This blog…

Why it matters and how to use it right So far, we’ve touched on how machine learning models are trained, validated, and deployed. Now, let’s dig deeper into one of the most important steps in the machine learning lifecycle: validation—more specifically, cross-validation. 🔍 Why model validation is critical Validation is…

So far, we’ve discussed how to train, test, and evaluate machine learning models. In this blog, let’s talk about the final—but one of the most important—steps: model deployment. You’ve built a great model. Now what? The real value of any machine learning (ML) model is unlocked only when it’s used…