Practice Feature Transformation (2.5.2) - Data Wrangling and Feature Engineering

Practice: Feature Transformation

Lesson 2.5.2Data Wrangling and Feature Engineering

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9 questions

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6 sample questions

Question 1.
What is feature transformation?
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Think about how we modify features before they go into a model.
Question 2.
Name a scaling method used in feature transformation.
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One method rescales to a specific range, and the other standardizes.
3Question 3.
What is the purpose of feature transformation?
- To reduce the number of features
- To alter feature distributions
- To visualize data
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Remember why we manipulate data before applying any algorithms.
4Question 4.
True or False: Scaling feature values can lead to more accurate model predictions.
- True
- False
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Consider why models might behave differently with varied scale features.
Question 5.
You are given a dataset with multiple features, some exhibiting strong skewness. Outline a step-by-step approach to handle these features for a regression model.
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Consider both transformation and scaling as critical components.
Question 6.
Imagine you are tasked with preparing a dataset with features ranging from 1 to 1000 versus features from 0 to 1 for a machine learning model. Describe how you would unify these features.
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How do scaling methods equalize varying feature ranges?

5 more questions in this section. Enroll free to practise them all.