Min-Max Normalization is a data preprocessing technique used to scale numerical features to a specific range, usually between 0 and 1. This method transforms the original data points into a normalized scale, making it easier for machine learning algorithms to process the data effectively. The formula used for min-max normalization is:
Xnorm = (X – Xmin) / (Xmax – Xmin)
Where:
- Xnorm is the normalized value.
- X is the original value.
- Xmin is the minimum value in the dataset.
- Xmax is the maximum value in the dataset.
By applying this transformation, the data is reshaped so that it fits within the desired range, which helps in reducing the effects of outliers and improving convergence during model training. Min-Max normalization is particularly useful for algorithms that are sensitive to the scale of data, such as neural networks and k-nearest neighbors.
However, it’s important to be aware that min-max normalization can be sensitive to outliers since they can significantly affect the minimum and maximum values. Therefore, it may be advisable to use other normalization techniques if the dataset contains extreme values.