A complete Python implementation and explanation of the calculations behind measuring feature importance in tree-based machine learning algorithms — The aim of this article is to familiarize the reader with how are the importance of features calculated in decision trees. Personally, I have not found an in-depth explanation of this concept and thus this article was born. All the code used in this article is publicly available and can…

Coding and explaining in depth the very popular and competition-winning gradient boosting algorithm using Python — The aim of this article is to explain every bit of the popular and oftentimes mysterious gradient boosting algorithm using Python code and visualizations. Gradient boosting is the key part of such competition-winning algorithms as CAT boost, ADA boost or XGBOOST thus knowing what is boosting, what is the gradient…

BubbleSort, SelectionSort, InsertionSort, MergeSort and QuickSort algorithms are explained in detail using code and visualizations — This article is a continuation of one of my previous articles: https://medium.com/codex/algorithms-with-python-introduction-97f3bf3cf247. It will be easier to follow the ideas presented in this article after reading the one provided above. In this article, I will present my coded implementations for the following popular sorting algorithms:

An introductory article to algorithms that showcases the bubble sort algorithm in Python — No matter the programming language, a key concept in all of them are algorithms. Programming languages come and go, but the theoretical and practical sides of algorithms stay the same. …

Software testing theory and practise in Python — This article is heavily influenced by the great book I read — “Unit Testing. Principles, Practices, and Patterns” by Vladimir Khorikov. I highly encourage anyone who wants to upgrade their skills in programming to add this book to their collection. All the examples in the book are given in the…

Coding the powerful algorithm in python using (mainly) arrays and loops — This article aims to demystify the popular random forest (here and throughout the text — RF) algorithm and show its principles by using graphs, code snippets and code outputs. The full implementation of the RF algorithm written by me in python can be accessed via: https://github.com/Eligijus112/decision-tree-python I highly encourage anyone…

Coding the popular regression tree algorithm in Python and explaining what’s under the hood — This article aims to present to the readers the code and the intuition behind the regression tree algorithm in python. I find looking through the code of an algorithm a very good educational tool to understand what is happening under the hood. …

Coding the popular algorithm using just NumPy and Pandas in Python and explaining what's under the hood — The aim of this article is to make all the parts of a decision tree classifier clear by walking through the code that implements the algorithm. The code uses only NumPy, Pandas and the standard python libraries. The full code can be accessed via https://github.com/Eligijus112/decision-tree-python

How to unlock the fast training of xgboost models in Python using a GPU — In this article, I want to go along with the steps that are needed to train xgboost models using a GPU and not the default CPU. Additionally, an analysis of how the training speeds are influenced by the sizes of the matrices and certain hyperparameters is presented as well. Feel…