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Amazon's electronics store division has over the last few months focused on getting customer feedback on their products, and marking them as safe/ unsafe. Their data science team has used decision trees for this.
The training set has these features: product ID, data, summary of feedback, detailed feedback and a binary safe/unsafe tag. During training, the data science team dropped any feedback records with missing features. The test set has a few records with missing "detailed feedback" field. What would you recommend?
A: Remove the test samples with missing detail feedback text fields
B: Generate synthetic data to fill in missing fields
C: Use an algorithm that handles missing data better than decision trees
D: Fill in the missing detailed feedback text field with the summary of feedback field.
Customized by subject matter experts to cater to job roles for all experience levels (fresh grads to 8-10 years) within your organization.
Adaface assessments have an average test taking rate of 86% as compared to an industry standard of 50%.
Adaface assessments have relevant questions that test for on-the-job skills. We do not expect candidates to solve puzzles/ trick questions.
We evaluated several of their competitors and found Adaface to be the most compelling. Great default library of questions that are designed to test for fit rather than memorization of algorithms.
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