Técnica de Normalización refers to a set of methods used in preprocesamiento de datos to adjust the scale of data values to a common range, enhancing the performance of aprendizaje automático models. By transforming features to a similar scale, these techniques help mitigate issues related to entrenamiento del modelo, such as convergence speed and predictive accuracy.
Existen varias técnicas comúnmente utilizadas técnicas de normalización:
- Normalización min-max: This method scales the data to a fixed range, typically [0, 1]. It is calculated using the formula: (X – min(X)) / (max(X) – min(X)), where X es el valor original de los datos.
- Normalización Z-score: Also known as standardization, this technique transforms the data based on the mean and standard deviation. The formula is: (X – μ) / σ, where μ is the mean and σ is the standard deviation of the dataset.
- Normalización Robusta: This approach uses the median and rango intercuartílico (IQR) to scale the data, making it less sensitive to outliers. The formula is: (X – median(X)) / IQR.
Normalization is particularly important in algorithms that rely on distance metrics, such as k-vecinos más cercanos (KNN) and gradient descent-based methods. If the features are not normalized, the model might give undue importance to variables with larger ranges, leading to biased predictions. By applying normalization techniques, practitioners can improve the interpretability and reliability of their models, ultimately leading to better decision-making based on the AI’s predictions.