In machine learning, data often comes in different ranges. For example, one column might have ages from 0 to 100, while another has salaries from 10,000 to 1,00,000. If we use these directly, the bigger numbers (like salaries) can have more influence, which is not fair. Min-Max Scaling helps fix this by shrinking all numbers to the same range, usually 0 to 1.
The formula for Min-Max Scaling is:
“Scaled Value”=(“Original Value” -“Minimum Value” )/(“Maximum Value” -“Minimum Value” )
Example: Suppose student marks range from 50 to 100. A mark of 75 would be scaled as:
“Scaled Value”=(75-50)/(100-50)=0.5
Now, 75 is represented as 0.5, which is easy for a machine to understand.
Min-Max Scaling makes sure all features have the same importance. It helps machine learning models like K-Nearest Neighbors, Neural Networks, and others to learn faster and give more accurate results.
In short, Min-Max Scaling is like resizing all numbers to a common scale so the computer can compare them fairly.
Min-Max Scaling is a data normalization technique used to rescale features to a fixed range, usually [0, 1]. Many machine learning algorithms, especially those based on distance (like K-Nearest Neighbors) or gradient optimization (like Neural Networks), perform better when input features are on a similar scale.
The formula for Min-Max Scaling is:
X_scaled =(X-X_min )/(X_max -X_min )
Where:
Example 1 (Student Marks): If exam marks range from 50 to 100, a mark of 75 is scaled as:
X_scaled =(75-50)/(100-50)=0.5
Example 2 (Real-Life):
Min-Max Scaling ensures that all features contribute equally to the model, reduces training time, and improves performance.
Documented by Nishu Kumari, Team edSlash.
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