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Exam DP-100 topic 2 question 28 discussion

Actual exam question from Microsoft's DP-100

Question #: 28
Topic #: 2

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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

Suggested Answer: B 🗳️

by GaborO at April 23, 2020, 8:55 a.m.

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GaborO

Highly Voted 4 years, 9 months ago

MDL doesn't normalize values, so I think the correct answer is B.

upvoted 30 times

meswapnilspal

4 years, 9 months ago

It is not just 'MDL', it is 'entropy MDL binning mode'.

upvoted 8 times

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trickerk

3 years, 7 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

4 years 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, 10 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 7 months ago

Selected Answer: A

It's deprecated and out of scope after 16th January 2025.

upvoted 1 times

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SanjayPatwardhan

7 months, 2 weeks 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

1 year, 2 months ago

Selected Answer: B

There is no use of entropy when normalizing data.

upvoted 1 times

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InversaRadice

1 year, 2 months ago

MDL is correct

upvoted 1 times

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Ahmed_Gehad

1 year, 6 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, 7 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

2 years 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

2 years ago

No. EMDL binning is used for feature selection, not for feature engineering.

upvoted 1 times

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Mebyxu

2 years ago

Correct answer is B

upvoted 1 times

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Edriv

2 years, 2 months ago

Option A (Yes)

upvoted 2 times

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zehraoneexam

2 years, 11 months ago

It is true. Because quantile is unsupervised .

upvoted 1 times

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synapse

2 years, 11 months ago

Selected Answer: B

MDL does not normalize

upvoted 2 times

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Maunik

3 years, 7 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, 10 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, 9 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, 5 months 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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