Evaluate the accuracy of an artificial intelligence system : Evaluate Accuracy of the DNN Model

The video tutorial explains the concept of deep neural networks (DNNs), focusing on a model with three hidden layers. It covers the process of building a fully connected DNN, implementing it on testing data, and evaluating its performance using a confusion matrix. The tutorial demonstrates how to calculate overall accuracy and other metrics, achieving an accuracy of over 88% in predicting unseen data labels.

What is a characteristic feature of the deep neural network described in the first section?

• It has a single hidden layer.

• It is not fully connected.

• It has three hidden layers.

• It uses a linear activation function.

What percentage of the data was used for testing the deep neural network model?

• 75%

• 100%

• 25%

• 50%

What is the purpose of creating a confusion matrix in the context of the deep neural network model?

• To reduce the model's complexity.

• To increase the number of hidden layers.

• To compare actual and predicted labels.

• To visualize the model architecture.

How is the overall accuracy of the model evaluated?

• By reducing the testing data size.

• By visualizing the model's architecture.

• By computing the mean of predicted and actual labels.

• By increasing the number of hidden layers.

What is the reported accuracy of the deep neural network model?

• 98%

• 78%

• 88%

• 68%