Normalization vs Standardization in ML Pipelines

In the realm of machine learning, particularly in the context of feature engineering, two essential techniques often come into play: normalization and standardization. Both methods are crucial for preparing data for machine learning models, but they serve different purposes and are applied in different scenarios. Understanding the distinctions between these two techniques is vital for any data scientist or software engineer preparing for technical interviews.

What is Normalization?

Normalization, also known as min-max scaling, is a technique used to scale the features of a dataset to a specific range, typically [0, 1]. This is particularly useful when the features have different units or scales. The formula for normalization is:

$X_{norm} = \frac{X - X_{min}}{X_{max} - X_{min}}$

Where:

• $X$ is the original value,
• $X_{min}$ is the minimum value of the feature,
• $X_{max}$ is the maximum value of the feature.

When to Use Normalization

• When the data is not normally distributed: Normalization is effective when the data does not follow a Gaussian distribution.
• When using algorithms sensitive to the scale of data: Algorithms like k-nearest neighbors (KNN) and neural networks benefit from normalized data, as they rely on distance calculations.

What is Standardization?

Standardization, on the other hand, transforms the data to have a mean of 0 and a standard deviation of 1. This process is also known as z-score normalization. The formula for standardization is:

$X_{std} = \frac{X - \mu}{\sigma}$

Where:

• $X$ is the original value,
• $\mu$ is the mean of the feature,
• $\sigma$ is the standard deviation of the feature.

When to Use Standardization

• When the data follows a Gaussian distribution: Standardization is particularly useful when the data is normally distributed.
• When using algorithms that assume normally distributed data: Algorithms like linear regression, logistic regression, and support vector machines (SVM) perform better with standardized data.

Key Differences

Feature	Normalization	Standardization
Scale	[0, 1]	Mean = 0, Std Dev = 1
Formula	Min-Max Scaling	Z-Score Normalization
Use Case	Non-normally distributed data	Normally distributed data
Algorithms	KNN, Neural Networks	Linear Regression, SVM

Conclusion

In summary, both normalization and standardization are essential techniques in the preprocessing phase of machine learning pipelines. Choosing the right method depends on the distribution of your data and the specific requirements of the algorithms you plan to use. Understanding these differences will not only enhance your data preprocessing skills but also prepare you for technical interviews in top tech companies.