XGBoost or Extreme Gradient Boosting is a highly efficient and scalable implementation of the gradient boosting framework. Developed by Tianqi Chen and used in many winning Kaggle machine learning competitions, XGBoost has become a go-to model for structured/tabular datasets due to its high performance, flexibility and built-in regularization capabilities. What is Boosting? Before understanding XGBoost, … Read more

Lasso Regression is one of the most powerful techniques in machine learning and statistics for regression analysis. It is especially useful when you have a large number of features and want to identify the most important ones. What is Regression? Before diving into Lasso Regression, let’s briefly understand what regression means. In simple words, regression … Read more

Introduction The Naive Bayes algorithm is a simple yet powerful classification technique based on Bayes’ Theorem with a strong assumption of feature independence. It is especially popular in text classification, spam detection, and sentiment analysis because of its high speed and decent accuracy even on relatively small datasets. This article dives deep into: What Is … Read more

Introduction to KNN The K-Nearest Neighbors in sklearn is one of the most straightforward and effective algorithms in machine learning. It belongs to the supervised learning category, meaning it requires labeled data to learn from. It’s commonly used for: Despite its simplicity, KNN in sklearn is surprisingly powerful and is often used as a baseline … Read more

If you’re just starting out with machine learning or statistics, you might already know about linear regression, a method used to predict continuous outcomes based on input features. But real-world data is often tricky. When features are closely related or there are too many, linear regression may struggle to make reliable predictions. That’s where Ridge … Read more

Introduction to SVM Support Vector Machine (SVM) is a type of supervised machine learning algorithm used primarily for classification tasks though it can also be used for regression. It works by finding a decision boundary called a hyperplane that best separates the data into different classes. Think of it as a smart line-drawer that aims … Read more

What is a Decision Tree in Machine Learning? A Decision Tree in Machine Learning is a popular predictive model that makes decisions by asking a series of yes-or-no questions. It works like a flowchart, where each internal node represents a decision based on a specific feature, each branch corresponds to an outcome of the decision, … Read more

Random Forest Regression If you’ve ever wondered how to make better predictions from complex data, Random Forest Regression might just be your new best friend. It’s a powerful machine learning algorithm used to predict continuous values (like house prices, stock values, or sales forecasts) and it does this by building not one, but many decision … Read more

What is Logistic Regression? Ever wondered how Netflix decides whether to recommend you a movie? Or how an email app figures out what’s spam? Behind the scenes, a simple but powerful algorithm called Logistic Regression often plays a key role. Despite the fancy name, Logistic Regression isn’t about predicting prices or numbers. Instead, it’s used … Read more

What is Linear Regression? Imagine you’re running a small chai shop, and you start noticing something: The more hours your shop stays open, the more cups of chai you sell. Now you start wondering: “Can I predict how many cups I’ll sell if I keep the shop open for, say, 10 hours?” This is where … Read more