Exam DP-100 topic 3 question 33 discussion

Actual exam question from Microsoft's DP-100

Question #: 33
Topic #: 3

You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.
You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.
Which parameter and value pair should you use in the AutoMLConfig class?

A. featurization = 'auto'
B. enable_voting_ensemble = True
C. task = 'classification'
D. exclude_nan_labels = True
E. enable_tf = True

Show Suggested Answer

Suggested Answer: A 🗳️

by ljljljlj at July 11, 2021, 2:04 p.m.

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datamijn

Highly Voted 3 years, 6 months ago

on 2/8/2021

upvoted 8 times

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Moshekwa

Highly Voted 3 years, 6 months ago

Chosen Answer is correct " In every automated machine learning experiment, automatic scaling and normalization techniques are applied to your data by default. These techniques are types of featurization that help certain algorithms that are sensitive to features on different scales. You can enable more featurization, such as missing-values imputation, encoding, and transforms." Source: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-features

upvoted 6 times

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sar77

Most Recent 2 weeks, 3 days ago

Selected Answer: A

In Azure Machine Learning's AutoMLConfig, setting featurization='auto' allows the system to automatically handle: Imputation of missing values: Numerical and categorical features with missing data will be filled in using suitable methods (like mean, median, mode, or advanced techniques). Encoding of categorical variables: Categorical features with few distinct values will be encoded automatically using one-hot encoding or ordinal encoding, depending on what suits the model best.

upvoted 1 times

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