What is the role of a “loss function” in machine learning?
a) To measure the error or difference between the predicted output and the actual output
b) To increase the accuracy of the model
c) To manage the training dataset
d) To split data into training and test sets
Answer:
a) To measure the error or difference between the predicted output and the actual output
Explanation:
A loss function is a mathematical function used in machine learning to quantify the difference between the predicted output and the actual target output. The goal of the model is to minimize this loss during training by adjusting the model parameters.
Common loss functions include Mean Squared Error (MSE) for regression tasks and Cross-Entropy Loss for classification tasks. The choice of loss function depends on the type of problem being solved and the desired outcome.
Minimizing the loss function ensures that the model learns the patterns in the data effectively, leading to better predictions and improved accuracy when applied to new, unseen data.