---
title: Structure of a Dataset
description: "![](https://sdscope.com/wp-content/uploads/2022/05/image-2-1024x234.png) Structure of a dataset A dataset is a simple, flat table containing historical data of the scenario for which predictions are to be made. Each row, referred to as an **example** or an **instance**, contains data for a single…"
url: "https://sdscope.com/doc/structure-of-a-dataset/"
updated: "2022-06-02"
category: 5. Data Understanding
---

# Structure of a Dataset

![](https://sdscope.com/wp-content/uploads/2022/05/image-2-1024x234.png)
Structure of a dataset

A dataset is a simple, flat table containing historical data of the scenario for which predictions are to be made. Each row, referred to as an **example** or an **instance**, contains data for a single observation about a problem. Each column, referred to as an **attribute**, a **variable** or a **dimension** contains a feature of the observation.

In supervised machine learning columns are divided into a set of **descriptive features** also called **standard features**, **regular features** or simply **features**, and a single **target feature** also called a **label** or an **outcome**. The dataset for an unsupervised learning task does not have a label, i.e., the dataset is "**unlabeled**".

The features that come directly from raw data sources, e.g., Origin, Dest, and DayOfWeek in the above diagram are referred to as **raw features** while those that do not exist in any raw data source or are created from one or more features in a data source, e.g., Duration (if calculated from “call start time” and “call end time”) and TimeOfDay (if derived from the actual time on the timestamp) are referred to as **derived features**.

- Join WhatsApp group [here](https://chat.whatsapp.com/G9evrFRKZwwJ0J0OHI6NI7)
- Join Facebook group [here](https://www.facebook.com/groups/artificialintelligenceforeveryone)