Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You are building a machine learning model to solve a classification problem. The model should identify if a patient has a tumor. Based on statistics, only 1.4% of scanned patients are identified positive for tumor.
You want to make sure the machine learning model is able to correctly identify patients with tumor. What is the technique to examine the effectiveness of the model?
A. Gradient Descent
B. Precision
C. Recall
D. Dropout
Answer: C.
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.
Recall: 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.
Gradient Descent is an optimization algorithm to find the minimal value of a function. Gradient descent is used to find the minimal minimal RMSE or cost function.
Dropout is a regularization method to remove random selection of fixed number of units in a neural network layer. More units dropped out, the stronger the regularization.
From the description, answers A & D are unrelated so they are incorrect.
Since very few cases are positively diagnosed with tumor, recall formula should be used to calculate the accuracy of the model. So, answer C 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: https://developers.google.com/machine-learning/glossary/
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