Author Archive | Jayita Gulati

In this article, you will learn how three widely used classifiers behave on class-imbalanced problems and the concrete tactics that make them work in practice.

In this article, you will learn a practical, end-to-end process for selecting a machine learning model that truly fits your problem, data, and stakeholders.

In this article, you will learn how bagging, boosting, and stacking work, when to use each, and how to apply them with practical Python examples. Topics we will cover include: Core ideas behind bagging, boosting, and stacking Step-by-step workflows and advantages of each method Concise, working code samples using scikit-learn Let’s not waste any more […]

This article explores what works in practice when it comes to feature scaling and what does not.

This article explains how each method works, their key differences, and how to decide which one best fits your project.

In this article, we’ll compare these three methods and see which one tends to work best for smaller datasets.

In this article, we’ll look at 7 useful NumPy tricks that can make your code shorter, faster, and easier to understand.

In this article, you’ll learn how to deploy a machine learning model using FastAPI and Docker.

This article explores the motivation, methodology, and practical applications of this hybrid strategy.