Practice Feature Transformation - 2.5.2 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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2.5.2 - Feature Transformation

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is feature transformation?

💡 Hint: Think about how we modify features before they go into a model.

Question 2

Easy

Name a scaling method used in feature transformation.

💡 Hint: One method rescales to a specific range, and the other standardizes.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the purpose of feature transformation?

  • To reduce the number of features
  • To alter feature distributions
  • To visualize data

💡 Hint: Remember why we manipulate data before applying any algorithms.

Question 2

True or False: Scaling feature values can lead to more accurate model predictions.

  • True
  • False

💡 Hint: Consider why models might behave differently with varied scale features.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Consider both transformation and scaling as critical components.

Question 2

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.

💡 Hint: How do scaling methods equalize varying feature ranges?

Challenge and get performance evaluation