Test Your Knowledge
Question 1 of 8
Which of the following best describes the primary purpose of data encoding?
To clean the dataset
To normalize numerical values
To transform categorical data into numerical format
To reduce the dataset size
Question 2 of 8
In which scenario would data encoding NOT be necessary?
When working with a dataset containing country names
When processing text for a Natural Language Processing task
When dealing with a dataset that only contains numerical values
When handling a dataset with product categories
Question 3 of 8
Which of the following is NOT a benefit of data encoding?
Improved data interpretability for machine learning models
Enhanced model performance
Reduction in the number of features in the dataset
Enabling efficient processing of categorical data
Question 4 of 8
When dealing with high-dimensional categorical data, what role does encoding play?
It increases the dimensionality further
It has no effect on dimensionality
It can help condense the data into a more manageable form
It only works on low-dimensional data
Question 5 of 8
Which encoding technique is most suitable for high-cardinality categorical data?
One-Hot Encoding
Binary Encoding
Label Encoding
Ordinal Encoding
Question 6 of 8
In Target Encoding, what value is used to replace the original categorical values?
The mode of the target variable for that category
The median of the target variable for that category
The mean of the target variable for that category
The frequency of the category in the dataset
Question 7 of 8
Which encoding technique is most appropriate for ordinal data?
Frequency Encoding
Question 8 of 8
What is a potential drawback of using Label Encoding for nominal data?
It creates too many new columns
It's computationally expensive
It may introduce unintended ordinal relationships
It doesn't work with machine learning algorithms