Data is an essential part of the field of machine learning. It refers to a set of observations or measurements that can be used to train a machine learning model. The quality and quantity of data available for training and testing play an important role in determining the performance of a machine learning model. Data can take various forms, such as numerical, categorical, or time-series data, and can come from various sources, such as databases, spreadsheets, or APIs. Machine learning algorithms use data to learn patterns and relationships between input variables and target outputs, which can then be used for predictive or classification tasks.
Data is generally divided into two types:
- labeled data
- unlabeled data
Labeled data includes the label or target variable that the model is trying to predict, while unlabeled data does not include the label or target variable. Data used in machine learning is usually numerical or categorical. Numeric data includes values that can be sorted and measured, such as age or income. Categorical data includes values that represent categories, such as gender or fruit type.