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ComputingLifeSci

A complete computational biology curriculum — 23 courses covering programming, databases, machine learning, genomics and research infrastructure. Written for biologists, not computer scientists.

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Programming for Biology

Python for Biologists
R for Biologists
Perl for Biologists
MATLAB for Biologists
SAS for Biologists
BioJava for Biologists

Data & Databases

SQL for Biological Databases
Bioinformatics Databases
Big Data Mining for Biologists

AI & Machine Learning

Machine Learning for Biologists
Artificial Intelligence for Biologists
MLOps for Biologists
AI Tools for the Bioinformatics Lab

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Machine Learning for Biologists82 chapters

Overview
Chapter 5.1 — Linear, Multiple, and Polynomial Regression
Chapter 5.2 — Logistic Regression and Naive Bayes
Chapter 5.3 — K-Nearest Neighbors (KNN) and Decision Trees
Chapter 5.4 — Random Forest and Extra Trees
Chapter 5.5 — Gradient Boosting: AdaBoost, XGBoost, LightGBM, and CatBoost
Chapter 5.6 — Support Vector Machines (SVM)
Chapter 5.7 — Neural Networks (Foundations Before Deep Learning)

/Machine Learning for Biologists/PART 5 — Supervised Learning

Chapter 22 of 826 min read

Chapter 5.4 — Random Forest and Extra Trees

1. Learning Objectives Understand how ensembles of trees fix the instability of single decision trees Distinguish Random Forest from Extra Trees Fit and interpret both, including feature importance 2. Theory Random…

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PreviousChapter 5.3 — K-Nearest Neighbors (KNN) and Decision Trees NextChapter 5.5 — Gradient Boosting: AdaBoost, XGBoost, LightGBM, and CatBoost