Evaluate the accuracy of an artificial intelligence system : Assessing the Accuracy of a Classification Model

The video tutorial explains how to assess the accuracy of a regression model by comparing predicted values with actual values. It introduces the concept of a confusion matrix, which helps summarize model performance. The tutorial also covers how to calculate model accuracy and provides a short code snippet for this purpose. Finally, it assigns homework to calculate accuracy for other models discussed in the section.

What does a value below 0.5 in the model's prediction indicate?

• More than 50% chance of admission

• Less than 50% chance of admission

• No chance of admission

• Exactly 50% chance of admission

What is the primary purpose of a confusion matrix?

• To confuse the model

• To summarize model predictions

• To increase model accuracy

• To predict future outcomes

In a confusion matrix, what does it mean if the model predicted 0 when the true value was 0?

• The model made a correct prediction

• The model was confused

• The model made an incorrect prediction

• The model needs more data

How is the accuracy of a model calculated using a confusion matrix?

• By subtracting incorrect predictions from correct predictions

• By dividing the number of correct predictions by the total number of predictions

• By adding all predictions together

• By multiplying correct predictions by incorrect predictions

What is the accuracy of the model discussed in the video?

• 80%

• 94.6%

• 50%

• 100%