Target Variable

What is a Target Variable? Target Variable Explained

The target variable, also known as the dependent variable or response variable, is a variable in a machine learning or statistical model that the model aims to predict or explain based on the input variables or features. It is the variable of interest that represents the outcome or the variable being studied.

In supervised learning tasks, this variable is the variable that is already known or observed for a set of data points, and the goal is to build a model that can accurately predict this variable for new, unseen data points. It can be continuous (regression problem) or categorical (classification problem).

Here are some examples of target variables in different domains:

Regression: In regression tasks, this variable is a continuous variable that represents a numerical quantity. For example, in predicting house prices, the target variable could be the sale price of a house, and the input variables could be features such as the number of bedrooms, square footage, location, etc.

Classification: In classification tasks, it is a categorical variable that represents different classes or categories. For example, in email spam classification, the target variable could be binary, with one class representing “spam" and the other representing “not spam." In multi-class classification, the target variable could have multiple categories, such as classifying images of animals into categories like “cat," “dog," or “bird."

Time Series Analysis: In time series analysis, this variable is typically a series of observations collected over time. For example, in forecasting stock prices, the target variable could be the daily closing price of a stock, and the input variables could be historical prices, trading volumes, etc.

Survival Analysis: In survival analysis, it is the time until an event of interest occurs, such as the time until a patient develops a disease or the time until a machine fails.

The selection of the target variable depends on the specific problem and the goals of the analysis. It is essential to carefully define and understand the target variable before proceeding with the modeling process. The quality and availability of data for the target variable play a crucial role in the model’s predictive performance.