Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN What is Loss Function

The video tutorial explains the concept of true and predicted labels in binary classification, where the true label is either 0 or 1, and the predicted label is a probability between 0 and 1. It introduces the loss function used in this context, focusing on cases where the true label is 1 or 0. The loss is calculated using logarithms, with specific outcomes for correct and incorrect predictions. The tutorial concludes with a concise explanation of the binary cross entropy loss function, which is widely used in binary classification tasks.

What is the range of predicted labels in binary classification?

• 1 to 10

• 0 to 100

• 0 to 1

• 0 to 0.5

In binary classification, what is the loss when the true label is 1 and the predicted probability is also close to 1?

• A large negative value

• Zero

• A large positive value

• One

What happens to the loss when the true label is 0 and the predicted probability is close to 1?

• The loss becomes zero

• The loss becomes a large positive value

• The loss remains unchanged

• The loss becomes a large negative value

Which of the following best describes the binary cross entropy loss function?

• It is used for multi-class classification

• It is used for regression tasks

• It measures the difference between true and predicted probabilities

• It calculates the sum of squared errors

What is the primary application of the binary cross entropy loss function?

• Binary classification

• Regression

• Multi-class classification

• Clustering