Why is standardization (z-score) often used in machine learning algorithms?
- Because it brings features to a comparable scale and is not bounded to a specific range
- Because it's easy to compute
- Because it's not affected by outliers
- Because it's the only way to handle numerical data
Standardization, also known as Z-score normalization, is a scaling technique that subtracts the mean and divides by the standard deviation. It is often used in machine learning as it can handle features that are measured in different units by bringing them to a comparable scale. It also doesn't bound values to a specific range.
Loading...
Related Quiz
- What type of data typically requires more complex statistical methods for analysis?
- A correlation coefficient of +1 between two variables indicates what kind of relationship?
- You've applied the IQR method on your dataset and found no outliers. However, you suspect there may be some. What could be your next steps?
- Which of the following scenarios is an example of Multicollinearity?
- How can pairwise deletion affect the correlation between variables?