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NEW QUESTION 49
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a new experiment in Azure Machine Learning Studio.
One class has a much smaller number of observations than the other classes in the training set.
You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Stratified split for the sampling mode.
Does the solution meet the goal?
- A. Yes
- B. No
Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
NEW QUESTION 50
You use Azure Machine Learning to train a model.
You must use a sampling method for tuning hyperparameters. The sampling method must pick samples based on how the model performed with previous samples.
You need to select a sampling method.
Which sampling method should you use?
- A. Bayesian
- B. Grid
- C. Random
NEW QUESTION 51
Your Azure Machine Learning workspace has a dataset named real_estate_dat a. A sample of the data in the dataset follows.
You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
NEW QUESTION 52
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