Practice Test 3 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
A medical facility is building a machine learning model to predict Addison disease, an uncommon disorder in which the body fails to produce enough amount of certain hormones. Since the disease is rare and only found in every 1 out of 100,000 people, the model should be measured to ensure that the model returns true positives while scanning a patient’s profile with Addison disease signs. Which of the following measures should be used to evaluate the accuracy of the model?
A. Dropout Regularization
B. Precision
C. Gradient Descent
D. Recall
Correct Answer: D
Dropout Regularization: It is a regularization method to remove a random selection of the fixed number of units in a neural network layer. More units dropped out, the stronger the regularization.
Precision is the formula to check how accurate the model is when most of the output are positives. In other words, if most of the output is yes.
Gradient Descent: It is an optimization algorithm to find the minimal value of a function. Gradient descent is used to find the minimal RMSE or cost function.
Recall: It is the formula to check how accurate the model is when most of the output are negatives. In other words, if most of the output is no.
From the explanation,
options A & C are unrelated so they are incorrect.
Since very few cases are positively diagnosed with Addison disease, recall formula should be used to calculate the accuracy of the model. So,
option D is the correct answer.
Source(s):
Precision & Recall:
https://developers.google.com/machine-learning/crash-course/classification/ precision-and-recall
Gradient Descent:
https://en.wikipedia.org/wiki/Gradient_descent
Dropout Regularization:
Comments are closed, but trackbacks and pingbacks are open.