In supervised learning, the target is the feature of a dataset you aim to understand better and predict using other features in the dataset. It's the variable you're trying to model or forecast.
Understanding the Target Variable
The target variable, sometimes called the dependent variable or response variable, is the main focus of a supervised learning task.
Key Aspects
- Prediction Goal: The target variable represents what you want to predict. For example, predicting housing prices, classifying emails as spam or not spam, or forecasting stock prices.
- Supervised Learning: Supervised learning algorithms learn from labeled data, where the labels are the values of the target variable.
- Feature Dependence: The algorithm learns the relationship between the other features (independent variables) and the target variable.
Examples of Target Variables
| Dataset | Target Variable |
|---|---|
| Housing Prices | Price of the house |
| Email Classification | Spam or Not Spam |
| Customer Churn Prediction | Whether a customer will churn (yes/no) |
| Medical Diagnosis | Presence or absence of a specific disease |
Role in Supervised Learning
In supervised learning, the algorithm learns a mapping from the input features to the target variable. This mapping is learned from a training dataset where both the features and the corresponding target values are known. Once trained, the algorithm can predict the target variable for new, unseen data.
Deriving the Target Variable
According to the reference, a supervised machine learning algorithm is generally used to derive the target variable. The algorithm looks for patterns in the dataset to learn the variable.
