In machine learning, the "target" refers to the variable that the model aims to predict or estimate based on the input data. It is also known as the dependent variable, response variable, outcome variable, or label, depending on the context and terminology used in different fields.

The target variable represents the output or outcome of interest in a supervised learning task. In supervised learning, the machine learning algorithm learns from the input data (features or independent variables) to make predictions or classifications about the target variable.

For example:

• In a regression problem, the target variable is continuous, such as predicting house prices, where the target variable might represent the price of a house based on various features like size, location, etc.
• In a classification problem, the target variable is categorical, such as classifying emails as spam or not spam based on various features extracted from the email content.

The primary goal in supervised learning is to build a predictive model that can accurately predict or classify the target variable using the patterns and relationships present in the input data. The quality of the predictions or classifications is evaluated based on how well the model performs in estimating or classifying the target variable on unseen data.