Naive Bayes remains a cornerstone of machine learning. Despite the “naive” label, it is a sophisticated probabilistic model that relies on solid statistical foundations. This article explores the m...

1. Explain how Logistic Regression model would work in a basketball game problem where we’re trying to predict the winning probability of a team based on no_of_hours they practiced. Here is an expl...

1. Derive the Normal Equation for Linear Regression The Normal Equation is a method for finding the optimal parameters in a linear regression model without resorting to an iterative optimization al...

1. Explain the concept of ‘bias variance tradeoff’ with a practical example. The Bias-Variance Tradeoff is one of the most fundamental concepts in machine learning. It’s the central challenge we fa...

1. Explain the concepts behind MCAR, MAR, and MNAR in context of missing data and elaborate on how to handle them. This is a fundamental topic in data preprocessing. Understanding why data is missi...

We’re almost at the end of our journey of A/B Testing. There are few concepts we need to understand before wrapping up. So, here we go. 1. Pre-requisites: The Statistical Foundation Before diving...

In the previous article, we’ve looked at hypothesis tests as tools to decide whether a difference is real or just random noise. But in product experimentation, we care about something deeper: Did t...

Prelude: The Engine of A/B Testing - The Central Limit Theorem (CLT) Before diving into null hypotheses and p-values, it is helpful to understand the mathematical phenomenon that makes A/B testing...

In the realm of statistics, p-values and confidence intervals are fundamental tools that help us make sense of data and draw conclusions, particularly when dealing with samples and trying to unders...

The Laplace distribution (often called the “Double Exponential” distribution) has two primary intuitions: The “Peakier” Bell Curve: Visually, it looks like two exponential distributions spliced...