Exam Professional Machine Learning Engineer All Questions

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Exam Professional Machine Learning Engineer topic 1 question 72 discussion

Actual exam question from Google's Professional Machine Learning Engineer

Question #: 72
Topic #: 1

[All Professional Machine Learning Engineer Questions]

You are building a linear model with over 100 input features, all with values between –1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

A. Use principal component analysis (PCA) to eliminate the least informative features.
B. Use L1 regularization to reduce the coefficients of uninformative features to 0.
C. After building your model, use Shapley values to determine which features are the most informative.
D. Use an iterative dropout technique to identify which features do not degrade the model when removed.

Show Suggested Answer

Suggested Answer: B 🗳️

by ares81 at Dec. 11, 2022, 4:15 p.m.

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hiromi

Highly Voted 2 years ago

Selected Answer: B

L1 regularization it's good for feature selection https://www.quora.com/How-does-the-L1-regularization-method-help-in-feature-selection https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

upvoted 8 times

ailiba

1 year, 10 months ago

but this is not a sparse input vector, just a high dimensional vector where many features are not relevant.

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ares81

Highly Voted 2 years ago

A. PCA reconfigures the features, so no. C. After building your model, so no. D. Dropout should be in the model and it doesn't tell us which features are informative or not. Big No! For me, it's B.

upvoted 5 times

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phani49

Most Recent 6 months, 3 weeks ago

Selected Answer: D

Even D is correct, but computationally not efficient. But in exam would opt for B if only 1 is correct

upvoted 1 times

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PhilipKoku

7 months ago

Selected Answer: B

B) L1 Regularisation

upvoted 1 times

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Liting

1 year, 6 months ago

Selected Answer: B

Went with B

upvoted 1 times

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M25

1 year, 8 months ago

Selected Answer: B

Went with B

upvoted 1 times

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Antmal

1 year, 9 months ago

Selected Answer: B

L1 regularization penalises weights in proportion to the sum of the absolute value of the weights. L1 regularization helps drive the weights of irrelevant or barely relevant features to exactly 0. A feature with a weight of 0 is effectively removed from the model. https://developers.google.com/machine-learning/glossary#L1_regularization

upvoted 1 times

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tavva_prudhvi

1 year, 9 months ago

Its B. See my explanations under the comments why its not C.

upvoted 1 times

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enghabeth

1 year, 11 months ago

Selected Answer: B

it's a best way, becouse you reduce features non relevant in this case non-informatives

upvoted 1 times

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behzadsw

2 years ago

Selected Answer: A

The features must be removed from the model. They are not removed when doing L1 regularization. PCA is used prior to training.

upvoted 2 times

tavva_prudhvi

1 year, 9 months ago

That is a good point. PCA is a technique used to reduce the dimensionality of the dataset by transforming the original features into a new set of uncorrelated features. This can help to eliminate the least informative features and reduce the computational burden of building a model with many input features. However, it is important to note that PCA does not necessarily remove the original features from the model, but rather transforms them into a new set of features. On the other hand, L1 regularization can effectively remove the impact of non-informative features by setting their coefficients to 0 during the model building process. Therefore, both techniques can be useful for addressing the issue of non-informative features in a linear model, depending on the specific needs of the problem.

upvoted 1 times

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