Escalonamento Min-Max, também conhecido como Normalização Min-Max, is a de pré-processamento de dados used to transform features to a specific range, usually between 0 and 1. This method is particularly useful in aprendizado de máquina and statistics onde algoritmos são sensíveis à escala das características de entrada.
A fórmula para Min-Max Scaling é:
X' = (X - X_{min}) / (X_{max} - X_{min})onde:
X'is the scaled value.Xis the original value.X_{min}is the minimum value of the feature in the dataset.X_{max}is the maximum value of the feature in the dataset.
This technique is beneficial because it preserves the relationships between the data points while scaling them to a uniform range. It ensures that each feature contributes equally to the distance calculations in algorithms such as k-vizinhos mais próximos métodos como KNN ou gradiente descendente.
However, Min-Max Scaling has its limitations. It is sensitive to outliers, which can significantly skew the scaled values if the minimum or maximum values are extreme. In such cases, alternative normalization methods, like Z-score normalization, may be more appropriate. Overall, Min-Max Scaling is a straightforward and effective method for escalonamento de características em várias aplicações de ciência de dados.