Discover one of the most used feature scaling techniques:
✨Min-Max Scaling✨
🧵 👇
✨Min-Max Scaling✨
🧵 👇
This is the simplest form of normalization.
👉 The idea is to scale the range of each feature (like age, salary, etc.) so that they all fit within a specific range, usually between 0 and 1. This can make it easier for machine learning algorithms to learn from the data.
👉 The idea is to scale the range of each feature (like age, salary, etc.) so that they all fit within a specific range, usually between 0 and 1. This can make it easier for machine learning algorithms to learn from the data.
🔧 Use it when the distribution of the feature is not Gaussian and you need values in a bounded interval. However, this method is sensitive to outliers.
📐 Mathematically, the simplest form of normalization for a feature x is calculated as:
To scale an original value or x, you subtract the minimum value from it, and then divide that by the range (Maximum Value - Minimum Value).
To scale an original value or x, you subtract the minimum value from it, and then divide that by the range (Maximum Value - Minimum Value).
You can achieve this in Python easily with the following snippet:
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