You are a machine learning specialist for a gaming software startup.
Your company is investigating ways to use machine learning to enhance its game software platform.
The team has selected the Amazon SageMaker platform for their machine learning efforts.
You are participating in the feature transformation process in preparation to create your machine learning models.
Instead of transforming your data before you use it in your SageMaker models, you and your team have decided to use the built-in transformations of SageMaker.
Specifically, you and your team have decided to use the built-in OneHotEncoder transformer to transform your categorical data. You have decided to drop one of the categories per feature because you suspect you may have perfectly collinear features.
Which of the following is NOT a drop methodology used in the OnHotEncoder transformer?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect.
The OneHotEncoder transformer has the following methodologies you can use to drop one of the categories per feature: None, first, array.
None is the default methodology.
Option B is correct.
The OneHotEncoder transformer has the following methodologies you can use to drop one of the categories per feature: None, first, array.
None is the default methodology.
The OneHotEncoder transformer drop parameter does not offer the last methodology.
Option C is incorrect.
The OneHotEncoder transformer has the following methodologies you can use to drop one of the categories per feature: None, first, array.
None is the default methodology.
Option D is incorrect.
The OneHotEncoder transformer has the following methodologies you can use to drop one of the categories per feature: None, first, array.
None is the default methodology.
Reference:
Please see the Amazon Machine Learning blog titled Preprocess input data before making predictions using Amazon SageMaker inference pipelines and Scikit-learn, and the Scikit-learn API documentation OneHotEncoder.
Amazon SageMaker provides a set of built-in transformers that can be used to transform data for machine learning models. One of these transformers is the OneHotEncoder transformer, which can be used to transform categorical data into numerical data that can be used in machine learning models. One potential issue with using the OneHotEncoder transformer is that it can create perfectly collinear features, which can negatively impact the performance of machine learning models.
To address this issue, the OneHotEncoder transformer in SageMaker provides several drop methodologies that can be used to drop one of the categories per feature. This helps to avoid creating perfectly collinear features.
The drop methodologies available in the OneHotEncoder transformer are:
A. None: This drop methodology does not drop any categories. All categories are encoded.
B. Last: This drop methodology drops the last category per feature.
C. Array: This drop methodology drops the category with the lowest frequency per feature.
D. First: This drop methodology drops the first category per feature.
Therefore, the answer to the question is A. None, because it is not a drop methodology used in the OneHotEncoder transformer.