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Chapter 4.1 — Data Cleaning: Missing Values and Outliers — Machine Learning for Biologists · ComputingLifeSci

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 14 of 826 min read

Chapter 4.1 — Data Cleaning: Missing Values and Outliers

1. Learning Objectives Understand why real biological data is messy Learn strategies for handling missing values Learn how to detect and handle outliers responsibly 2. Theory Real biological datasets are rarely clean.…

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PreviousChapter 3.5 — Statistics: Hypothesis Testing, Correlation, Covariance, Variance, and Sampling NextChapter 4.2 — Normalization, Standardization, Encoding, and Scaling