Normalization vs Standardization, When to Use What?
Normalization is a method of adjusting values to a 0-1
range, ensuring no specific value disproportionately influences the result. Standardization adjusts data to have a mean of 0
and a standard deviation of 1
, aligning it to follow a normal distribution.
In this lesson, you'll learn the differences between normalization and standardization and the appropriate use cases for each.
How Do Normalization and Standardization Differ?
Criterion | Normalization | Standardization |
---|---|---|
Transformation Method | Adjustment to 0-1 range (Min-Max Scaling) | Adjustment to mean 0, standard deviation 1 (Z-score Scaling) |
Sensitivity to Outliers | Sensitive to outliers | Less sensitive to outliers |
Use Cases | Image processing, deep learning | Statistical analysis, regression, PCA |
Applicability | When the range of values matters | When data follows a normal distribution |
How to Choose Between Normalization and Standardization in Practice
Normalization and standardization have their respective advantages and should be chosen based on the characteristics of the data and the purpose of the analysis.
Here’s how to decide between normalization and standardization in practical scenarios.
If You Are Doing Deep Learning
→ Normalization
is generally more favorable (neurons learn more stably within the 0-1 range)
If You Are Performing Statistical Analysis
→ Standardization
is suitable (adjusts data to distribute around the mean)
If There Are Many Outliers in the Data
→ Standardization
is preferable (extreme values have less impact)
If Maintaining the Range of Data Is Important
→ Normalization
is used (maintains the minimum-maximum range)
In the next lesson, we'll explore categorical data encoding
.
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