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In the context of feature engineering, what does 'one-hot encoding' achieve?

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Question: In the context of feature engineering, what does \'one-hot encoding\' achieve?

Options:

  1. Reduces dimensionality
  2. Converts categorical variables into a numerical format
  3. Eliminates multicollinearity
  4. Increases the number of features exponentially

Correct Answer: Converts categorical variables into a numerical format

Solution: 

One-hot encoding converts categorical variables into a numerical format by creating binary columns for each category, allowing algorithms to process them effectively.

In the context of feature engineering, what does 'one-hot encoding' achieve?

Practice Questions

Q1
In the context of feature engineering, what does 'one-hot encoding' achieve?
  1. Reduces dimensionality
  2. Converts categorical variables into a numerical format
  3. Eliminates multicollinearity
  4. Increases the number of features exponentially

Questions & Step-by-Step Solutions

In the context of feature engineering, what does 'one-hot encoding' achieve?
  • Step 1: Identify the categorical variable you want to convert. This is a variable that has categories or labels, like 'color' with values 'red', 'blue', and 'green'.
  • Step 2: List all unique categories of the variable. For 'color', the unique categories are 'red', 'blue', and 'green'.
  • Step 3: Create a new binary column for each unique category. For 'color', create three new columns: 'is_red', 'is_blue', and 'is_green'.
  • Step 4: For each row in your data, fill in the new columns with 1s and 0s. If the color is 'red', then 'is_red' gets 1, and 'is_blue' and 'is_green' get 0.
  • Step 5: Repeat this process for all categorical variables you want to convert.
  • One-Hot Encoding – A technique to convert categorical variables into a numerical format by creating binary columns for each category.
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