What term denotes the variable we are trying to explain in a predictive model?

What term denotes the variable we are trying to explain in a predictive model?

• A. Features
• B. Target variable
• C. Multicollinearity
• D. Outliers

Answer: (B)

The main idea here is the variable you are trying to predict from the inputs. This is called the target variable. It represents the outcome the model aims to explain or forecast, and it can be a continuous value in regression or a category in classification. The inputs used to explain or predict it are the features. For example, in predicting house prices, the target variable is the price you want to predict, while features like size, location, and age are the predictors. Concepts like multicollinearity refer to relationships among the inputs themselves, and outliers are unusual observations that can distort the model, but they’re not the variable you’re trying to predict.

The main idea here is the variable you are trying to predict from the inputs. This is called the target variable. It represents the outcome the model aims to explain or forecast, and it can be a continuous value in regression or a category in classification. The inputs used to explain or predict it are the features. For example, in predicting house prices, the target variable is the price you want to predict, while features like size, location, and age are the predictors. Concepts like multicollinearity refer to relationships among the inputs themselves, and outliers are unusual observations that can distort the model, but they’re not the variable you’re trying to predict.