Data Representation
The main objective of ML is to build models by interpreting data. To do so, it is highly important to feed the data in a way that is readable by the computer. To feed data into a scikit-learn model, it must be represented as a table or matrix of the required dimensions, which we will discuss in the following section.
Tables of Data
Most tables that are fed into ML problems are two-dimensional, meaning that they contain rows and columns. Conventionally, each row represents an observation (an instance), whereas each column represents a characteristic (feature) of each observation.
The following table is a fragment of a sample dataset of scikit-learn. The purpose of the dataset is to differentiate from among three types of iris plants based on their characteristics. Hence, in the following table, each row embodies a plant and each column denotes the value of that feature for every plant:
Figure 1.2: A table showing the first 10 instances...
