Practice Test 1 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You are building a machine learning classification model using TensorFlow. You trained the model by using 70% of the total set available for training, validation and testing. After testing the model, AUC returned from the test results was 0.68. The main issue here is due to overfitting. You want to increase the AUC for better accuracy of results. What should you do?
A. Increase regularization.
B. Reduce samples used for training.
C. Reduce regularization.
D. Increase feature parameters.
Answer: A.
Description:
AUC stands for “Area under the ROC Curve.” That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1):
AUC provides an aggregate measure of performance across all possible classification thresholds. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.
The problem in this scenario is due to overfitting. To solve the overfitting problem, you need to:
- Increase the training set.
- Decrease features parameters.
- Increase regularization.
Source(s):
Classification: ROC Curve and AUC:
https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc
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