Min-Max Scaling 편집하기

'''Min-Max Scaling''' is a data normalization technique used to scale features to a fixed range, typically [0, 1]. It ensures that all features contribute equally to the analysis or model by transforming the original values proportionally to fit within the specified range. Min-Max Scaling is widely used in data preprocessing for machine learning and statistical analysis.
==Overview==
Min-Max Scaling transforms the data linearly by rescaling each value based on the feature's minimum and maximum values. This technique is particularly useful when the data needs to be bounded within a specific range, such as [0, 1] or [-1, 1].

Key characteristics:
*Ensures all values are within the specified range.
*Preserves the relationships between original values.
*Sensitive to outliers, as extreme values can distort the scaling.
==Formula==
The formula for Min-Max Scaling is:
<blockquote>X' = (X - X_min) / (X_max - X_min)</blockquote>
Where:
*X: The original value.
*X_min: The minimum value of the feature.
* X_max: The maximum value of the feature.
*X': The scaled value within the range [0, 1].
==Example ==
Consider a dataset with the following values:
{| class="wikitable"
!Original Value!!Min-Max Scaled Value (Range: 0 to 1)
|-
|10 || 0.0
|-
|15||0.5
|-
|20||1.0
|}
===Steps===
#Find the minimum (X_min) and maximum (X_max) values:
#* X_min = 10
#*X_max = 20
#Apply the formula:
#*For X = 10: X' = (10 - 10) / (20 - 10) = 0.0
#*For X = 15: X' = (15 - 10) / (20 - 10) = 0.5
#*For X = 20: X' = (20 - 10) / (20 - 10) = 1.0

==Relationship to Normalization ==
Min-Max Scaling is a specific method of normalization, as it rescales data to a defined range, typically [0, 1]. In the broader context of data preprocessing:
*'''Normalization:'''
**Refers to the process of transforming data to a specific range or scale. It is a general term encompassing various techniques.
* '''Min-Max Scaling:'''
**A specific technique within normalization that linearly rescales data based on the minimum and maximum values of the feature.

In essence:
*Normalization is the overarching concept.
*Min-Max Scaling is one of the approaches used to achieve normalization.

Other normalization techniques, such as Z-Score Normalization and Decimal Scaling, may be preferred in scenarios where handling outliers or achieving a standard distribution is necessary. However, Min-Max Scaling is particularly effective for bounded transformations like [0, 1].

==Applications==
Min-Max Scaling is commonly used in:
*'''Machine Learning:'''
**Preprocessing features for models sensitive to feature scale, such as neural networks and gradient descent-based algorithms.
*'''Image Processing:'''
**Normalizing pixel intensities to the range [0, 1].
*'''Finance:'''
**Scaling stock prices or other financial metrics for analysis and comparison.
*'''Data Visualization:'''
**Ensuring uniform scale across variables for clear and consistent visualization.
==Advantages==
*Ensures all features contribute equally to the model.
*Simple and computationally efficient.
*Suitable for features with known bounds.
==Limitations==
*Sensitive to outliers, as extreme values can skew the scaling.
*Requires knowledge of the minimum and maximum values for consistent scaling.
*Does not standardize the data (e.g., mean ≠ 0, standard deviation ≠ 1).
==Python Code Example==
<syntaxhighlight lang="python">
import numpy as np
from sklearn.preprocessing import MinMaxScaler

# Example data
data = np.array([[10], [15], [20]])

# Min-Max Scaling
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)

print("Original Data:", data.flatten())
print("Scaled Data:", scaled_data.flatten())
</syntaxhighlight>
==See Also==
*[[Normalization]]
*[[Feature Scaling]]
*[[Standardization]]
*[[Data Preprocessing]]
*[[Machine Learning]]
[[Category:Data Science]]