ML Foundations
Imagine you have trained a complex gradient-boosted tree to predict house prices. It achieves state-of-the-art accuracy, but when it predicts […]
SHAP (Shapley Additive Explanations): From Intuition to Implementation Read More »
Before LightGBM entered the scene, another algorithm reigned supreme in the world of machine learning competitions and industrial applications: XGBoost.
XGBoost: Extreme Gradient Boosting — A Complete Deep Dive Read More »
Adjusted R-squared is one of those metrics that shows up early in regression, but it often feels like a small
Adjusted R-Squared: Why, When, and How to Use It Read More »
When you train a regression model, you usually want to answer a simple question: How well does this model explain
R-Squared (\(R^2\)) Explained: How To Interpret The Goodness Of Fit In Regression Models Read More »
Logistic Regression is one of the simplest and most widely used building blocks in machine learning. In this article, we
Logistic Regression in PyTorch: From Intuition to Implementation Read More »
Gradient Boosting is more than just another algorithm; it is a fundamental concept that combines several key ideas in machine learning: the wisdom of ensembles, the precision of gradient descent, and the power of iterative improvement. By building a model that learns from its mistakes in a structured, mathematically-grounded way, it has rightfully earned its place as one of the most effective and versatile tools in a data scientist’s toolkit.
Gradient Boosting: Building Powerful Models by Correcting Mistakes Read More »
FastText is a testament to the power of simple ideas. By treating words as compositions of their parts, it elegantly solves the out-of-vocabulary problem and provides a robust way to represent language. Its speed and efficiency, for both embedding generation and classification, make it a go-to tool for NLP practitioners.
What is FastText? Quick, Efficient Word Embeddings and Text Models Read More »
Target encoding, also known as mean encoding or impact encoding, is a powerful feature engineering technique used to transform high-cardinality
Target Encoding: A Comprehensive Guide Read More »
Extremely Randomized Trees (Extra-Trees) is a machine learning ensemble method that builds upon Random Forests construction process. Unlike Random Forests,
Understanding Extra-Trees: A Faster Alternative to Random Forests Read More »
Random forest is a powerful ensemble learning algorithm used for both classification and regression tasks. It operates by constructing multiple
The Complete Guide to Random Forest: Building, Tuning, and Interpreting Results Read More »
The variance of a Random Forest (RF) is a critical measure of its stability and generalization performance. While individual decision
How Tree Correlation Impacts Random Forest Variance: A Deep Dive Read More »
Imagine you’re teaching a robot to write poetry. You give it a prompt, and it generates a poem. But how
How to Evaluate Text Generation: BLEU and ROUGE Explained with Examples Read More »
