13 May Dependent and Independent Variables in Machine Learning

In machine learning, dependent and independent variables are key concepts used to describe the relationship between input features and the target output:

Independent Variables are the inputs or features that influence or predict an outcome. They are what you feed into the model to make predictions. For example, in predicting house prices, features like the size of the house, number of bedrooms, and location are independent variables.

Dependent Variable is the output or the target variable that the model is trying to predict. It depends on the independent variables. In the house price example, the actual price of the house is the dependent variable.

Think of it like cause and effect. The independent variables are the “cause,” and the dependent variable is the “effect.” The goal of a machine learning model is to learn how independent variables influence the dependent variable and make accurate predictions based on this relationship.

Let us now understand them:

Independent Variables (Features)

• Independent Variables are called input variables, features, or predictors.
• These are the variables used to predict or explain the dependent variable.
• Represented as X in machine learning models.
• Example: Predicting house prices: Independent variables could be size (sq. ft.), location, number of bedrooms, etc.

Dependent Variable (Target)

• Dependent Variables are called the output variable, response variable, or label.
• This is the variable we want to predict or model.
• Represented as y in machine learning models.
• Example: Predicting house prices: The dependent variable is the price of the house.

Dependent vs Independent Variables (Differences)

Real-life example

Let us see some quick examples to understand what exactly is a dependent and independent variable:

Example in Regression

Suppose we want to predict a student’s exam score based on study hours:

• Independent Variable (X): Study hours (e.g., 2, 5, 1, …)
• Dependent Variable (y): Exam score (e.g., 75, 90, 60, …)

Example in Classification

Predicting spam vs. not spam based on email content:

• Independent Variables (X): Words, sender, subject line
• Dependent Variable (y): “Spam” (1) or “Not Spam” (0)

Summary

• Independent variables (X) → Input features → What the model uses to make predictions.
• Dependent variable (y) → Target output → What the model tries to predict.

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