nerdexam
Microsoft

DP-100 · Question #30

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…

The correct answer is B. No. Quantiles binning creates bins with approximately equal numbers of samples, which improves on Equal Width, but it still ignores the relationship between the feature bins and the target variable. For a supervised task - predicting a target column - optimal binning should account…

Design and prepare a machine learning solution

Question

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 a data scientist using Azure Machine Learning Studio. You need to normalize values to produce an output column into bins to predict a target column. Solution: Apply a Quantiles binning mode with a PQuantile normalization. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(26 responses)
  • A
    23% (6)
  • B
    77% (20)

Explanation

Quantiles binning creates bins with approximately equal numbers of samples, which improves on Equal Width, but it still ignores the relationship between the feature bins and the target variable. For a supervised task - predicting a target column - optimal binning should account for how well bin boundaries separate target classes. Methods like Entropy MDL (Minimum Description Length) determine bin boundaries by maximizing information gain with respect to the target, making the bins directly informative for prediction. Quantiles with PQuantile normalization is an unsupervised binning approach that does not optimize for target predictability, so it does not fully meet the goal.

Topics

#Data Preprocessing#Feature Engineering#Azure Machine Learning#Data Transformation

Community Discussion

No community discussion yet for this question.

Sign up to join the discussion
Full DP-100 Practice