Loading chapter

Loading courses

ComputingLifeSci

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

support@computinglifesci.com 8052536359

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

Home All courses Roadmaps Videos BINC & GATE notes Career Know where you can work Pricing About Contact us Terms Privacy Refunds & delivery

Loading chapter

Machine Learning for Biologists82 chapters

Chapter 4.1 — Data Cleaning: Missing Values and Outliers
Chapter 4.2 — Normalization, Standardization, Encoding, and Scaling
Chapter 4.3 — Feature Engineering and Feature Selection
Chapter 4.4 — Dimensionality Reduction: PCA, t-SNE, and UMAP

/Machine Learning for Biologists/PART 4 — Data Preprocessing

Chapter 16 of 826 min read

Chapter 4.3 — Feature Engineering and Feature Selection

1. Learning Objectives Understand the difference between feature engineering and feature selection Learn common feature engineering techniques for biological data Learn common feature selection methods and when to use…

Sign in to read this

“Chapter 4.3 — Feature Engineering and Feature Selection” is for subscribers. Start with a free account — it takes a name and an email — then subscribe for $1 a month to open it.

Python and R courses free in full

First two chapters of every other course

Everything else — including this one — for $1 a month

Cancel any time

Create a free account I already have one

PreviousChapter 4.2 — Normalization, Standardization, Encoding, and Scaling NextChapter 4.4 — Dimensionality Reduction: PCA, t-SNE, and UMAP