University of Michigan - Ann Arbor

Before using a dataset for a machine learning task, it is important to examine it first. This initial inspection helps you (1) understand how much data is missing, (2) judge whether the features need further cleaning, and (3) decide which techniques are appropriate for analyzing the data.

Examining a Dataset Before Machine Learning

You are carrying out error analysis and counting up what errors the algorithm makes. Which of these datasets do you think you should manually go through and carefully examine, one image at a time?

In data science, missing values (often represented programmatically as `NaN`) occur when entries in a dataset are empty or undefined, resulting from missing data collection or parsing errors. These incomplete records are a persistent challenge in data preprocessing and must be deliberately handled before training a model, typically through either imputation (estimating replacement values) or deletion (discarding the incomplete rows or columns).

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