Error is the difference between the actual value and Predicted value and the goal is to reduce this difference.TrueFalse

The statement is True.

Error, in statistical or machine learning contexts, indeed refers to the difference between the actual value (what is observed) and the predicted value (what the model estimates). This difference is often quantified using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE). The primary objective in modeling, whether it be in regression, classification, or other predictive tasks, is to minimize this error. By reducing the discrepancy between actual and predicted values, one enhances the accuracy and reliability of the model's predictions. Consequently, effective error reduction leads to improved model performance and ultimately allows for better decision-making based on the insights derived from the model.