Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You are building a machine learning model to solve a binary classification problem. The model is going to predict the likelihood of a customer to be using a fraudulent credit card when purchasing online.
Since there is a very small fraction of purchase transactions are proved to be fraudulent, more than 99% of the purchase transactions are valid.
You want to make sure the machine learning model is able to identify the fraudulent transactions. What is the technique to examine the effectiveness of the model?
A. Gradient Descent
B. Recall
C. Feature engineering
D. Precision
Answer: B.
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.
Feature Engineering is the process of deciding which data is important for the model.
From the description, answers A & C are incorrect. It leaves us with B & D.
Since the scenario mentions very little likelihood a transaction can be fraudulent. There are more “no” than “yes” means more negative than positive. Hence, to calculate the effectiveness of the model, you should use recall formula.
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
Feature Engineering: https://cloud.google.com/ml-engine/docs/tensorflow/data-prep
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