Out-of-Bag (OOB) Score in the Random Forest Algorithm
ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction While trying to make a better predictive model, we come …
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Accumulated Local Effects (ALE) – Feature Effects Global Interpretability
ArticleVideos This article was published as a part of the Data Science Blogathon. This article happens to be a continuation of my last article …
Machine Learning in Cyber Security — Malicious Software Installation
ArticleVideos Introduction Monitoring of user activities performed by local administrators is always a challenge for SOC analysts and security professionals. Most of the security …
Decision Tree vs. Random Forest – Which Algorithm Should you Use?
ArticleVideos A Simple Analogy to Explain Decision Tree vs. Random Forest Let’s start with a thought experiment that will illustrate the difference between a …
A Beginner’s Guide to Random Forest Hyperparameter Tuning
ArticleVideos Introduction to Random Forest What’s the first image that comes to your mind when you think about Random Forest? It conjures up images …
Data Science Project: Scraping YouTube Data using Python and Selenium to Classify Videos
ArticleVideos This article was submitted as part of Analytics Vidhya’s Internship Challenge. Introduction I’m an avid YouTube user. The sheer amount of content I …
Building a Random Forest from Scratch & Understanding Real-World Data Products (ML for Programmers – Part 3)
This article covers different industry applications where a machine learning model can be implemented and necessary steps to follow in building a model.
An Introduction to Random Forest using the fastai Library (Machine Learning for Programmers – Part 1)
This article provides a comprehensive summary of fast.ai’s machine learning course. It’s a deep dive into the inner workings of the Random Forest algorithm!
A Comprehensive Guide to Ensemble Learning (with Python codes)
Ensemble models combine predictions from multiple models to improve the overall performance. Bagging and Boosting are two important ensemble learning techniques. Important techniques random forest, gradient boosting, XGBoost, CatBoost, LightBoost
Data Scientists built a Random Forest Model to predict the World Cup 2018 Winner
Researchers from the University of Dortmund built a random forest model to predict the outcome of every single game of this year’s World Cup.
