Min-Max scaling

Information

• Min-Max scaling is a technique used to scale numerical features to a fixed range of [0, 1].
• It is a linear scaling method that linearly transforms each feature to the specified range.
• The formula used for Min-Max scaling is: X_scaled = (X - X_min) / (X_max - X_min) where X_scaled is the scaled feature value, X is the original feature value, X_min is the minimum value of the feature, and X_max is the maximum value of the feature.
• The scaled feature value for a minimum value is always 0, and the scaled feature value for a maximum value is always 1.
• Min-Max scaling is useful in machine learning algorithms that use distance measurements, such as K-Nearest Neighbors and clustering algorithms.
• Min-Max scaling can be sensitive to outliers in the data, as it maps the range of values to the [0, 1] interval, which can compress the majority of the data into a narrow range.