What Are Dependent and Independent Features in Machine Learning?

Tiara Williamson
Tiara WilliamsonAnswered

The dependent variable is the one being trained on, whereas the independent variables are those being used to train the model.

Difference between the independent variable and dependent

The dependent variable is the variable about which predictions or explanations are being sought. Supervised learning involves making predictions about a target variable based on the value of a collection of input variables. In a regression issue, the dependent variable may be the cost of a home, while the independent variables could be the number of bedrooms, the size of the lot, the neighborhood, and so on.

In contrast, independent variables (sometimes called predictor variables) are those that are used to generate predictions about or to account for the variation in the dependent variable (the goal). Variables might be quantitative or qualitative and can have either a continuous or categorical structure. Data may be altered or scaled in order to increase its predictive value.

The dependent or independent variable

The relationship between the dependent and independent variables is often modeled using statistical or machine learning techniques. Methods that try to capture the connections between the variables include linear regression, logistic regression, decision trees, and others.

As the accuracy and dependability of an ML model’s predictions are highly reliant on the quality and relevance of the independent variables included in the model, it is crucial to choose these variables with care. Domain expertise may guide the choice of independent variables; alternatively, feature selection or feature engineering methods can be employed to zero in on the most informative variables.

Key takeaways

Independent and dependent variables are important concepts in machine learning, as they represent the target variable and input variables used to make predictions or explain the variation in the target variable. To build trustworthy and accurate prediction models, it is essential to first comprehend the interplay between these factors and then choose the most useful independent variables.

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