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Exam DP-100 topic 2 question 28 discussion

Actual exam question from Microsoft's DP-100
Question #: 28
Topic #: 2
[All DP-100 Questions]

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.
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You are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?

  • A. Yes
  • B. No
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by GaborO at April 23, 2020, 8:55 a.m.

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GaborO
Highly Voted 4 years, 6 months ago
MDL doesn't normalize values, so I think the correct answer is B.
upvoted 30 times
meswapnilspal
4 years, 5 months ago
It is not just 'MDL', it is 'entropy MDL binning mode'.
upvoted 8 times
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trickerk
3 years, 3 months ago
I agree: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins See the table "Module parameters" Name Range Type Default Description Binning mode List QuantizationMode Quantiles Choose a binning method Quantile normalization any BinningNormalization Choose the method for normalizing quantiles For BinningNormalization should be used Quantile Normalization.
upvoted 4 times
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febriyanasn
3 years, 8 months ago
agree with GaborO, it should be Quantile Normalization and not Entropy MDL "If you select the Quantiles binning mode, use the Quantile normalization option to determine how values are normalized prior to sorting into quantiles. Note that normalizing values transforms the values, but does not affect the final number of bins" https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins#bkmk_Effects
upvoted 4 times
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David_Tadeu
Highly Voted 2 years, 7 months ago
Selected Answer: B
According to https://docs.microsoft.com/en-us/previous-versions/azure/machine-learning/studio-module-reference/group-data-into-bins, you can specify the following binning modes: - Entropy MDL - Quantiles - Equal Width - Custom Edges - Equal Width with Custom Start and Stop From all of these, the only binning mode which supports normalization is Quantiles. In particular, Entropy MDL does NOT support normalization.
upvoted 8 times
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astone42
Most Recent 3 months, 2 weeks ago
Selected Answer: A
It's deprecated and out of scope after 16th January 2025.
upvoted 1 times
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SanjayPatwardhan
4 months ago
Selected Answer: A
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins
upvoted 1 times
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NullVoider_0
10 months, 2 weeks ago
Selected Answer: B
There is no use of entropy when normalizing data.
upvoted 1 times
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InversaRadice
11 months ago
MDL is correct
upvoted 1 times
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Ahmed_Gehad
1 year, 3 months ago
Selected Answer: B
The answer is B. No. Entropy Minimum Description Length (MDL) binning is a technique that can be used to group values into bins. However, it is not a normalization technique. Normalization is a technique that is used to scale values so that they have a similar range. In this case, the goal is to normalize values to produce a feature column grouped into bins. However, the solution of applying an Entropy MDL binning mode will not achieve this goal. Instead, you should use a normalization technique, such as min-max normalization or z-score normalization.
upvoted 1 times
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pranav33
1 year, 4 months ago
B. No The solution described does not meet the goal of normalizing values to produce a feature column grouped into bins. Entropy Minimum Description Length (MDL) is a criterion used for model selection and not specifically for binning or normalization of data. MDL is typically used for tasks like feature selection or model complexity estimation, but not for creating bins. To achieve the goal of normalizing values and creating bins, other techniques such as equal-width binning or equal-frequency binning can be used.
upvoted 1 times
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mamau
1 year, 8 months ago
Correct answer B https://learn.microsoft.com/en-us/previous-versions/azure/machine-learning/studio-module-reference/group-data-into-bins
upvoted 1 times
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phdykd
1 year, 8 months ago
No. EMDL binning is used for feature selection, not for feature engineering.
upvoted 1 times
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Mebyxu
1 year, 9 months ago
Correct answer is B
upvoted 1 times
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Edriv
1 year, 10 months ago
Option A (Yes)
upvoted 2 times
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zehraoneexam
2 years, 7 months ago
It is true. Because quantile is unsupervised .
upvoted 1 times
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synapse
2 years, 7 months ago
Selected Answer: B
MDL does not normalize
upvoted 2 times
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Maunik
3 years, 3 months ago
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins - Correct answer seems to be A. In in graph it mentions normalized data in to bins
upvoted 2 times
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Haet
3 years, 6 months ago
The answer is Entropy MDL reason if you see the explanation it says they want feature column group into bins this is done by only Entropy MDL and not in Quantile Normalization.
upvoted 2 times
allanm
3 years, 5 months ago
Incorrect. The question requirements states that the data has to be normalised and then binned. You cannot do Normalisation with entropy MDL, it must be quantile normalisation first.
upvoted 2 times
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111ssy
4 years, 1 month ago
Entropy MDL: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized. https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins
upvoted 3 times
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