aiFundamentalsMachineLearningChapter2Desc

lesson1Title

lesson2Title

lesson3Title

lesson4Title

lesson5Title

lesson6Title

lesson7Title

lesson8Title

lesson9Title

lesson10Title

lesson11Title

lesson12Title

lesson13Title

lesson14Title

lesson15Title

lesson16Title

lesson17Title

lesson18Title

lesson19Title

lesson20Title

lesson21Title

aiFundamentalsMachineLearningChapter2Title

aiFundamentalsMachineLearningChapter1Desc

lesson22Title

lesson23Title

aiFundamentalsMachineLearningChapter1Title

aiFundamentalsMachineLearningChapter3Desc

lesson24Title

aiFundamentalsMachineLearningChapter3Title

aiFundamentalsMachineLearningChapter4Desc

aiFundamentalsMachineLearningChapter4Title

# 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.  

<br />

## 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 |

<br />

## 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.

<br />

### 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)  

<br />

In the next lesson, we'll explore `categorical data encoding`.

정규화는 데이터의 범위를 0과 1 사이로 조정하는 방법으로, 이상치에 민감할 수 있습니다.

### 다음 중 빈칸에 들어갈 가장 적절한 단어는 무엇인가요?

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