Técnica de Normalização refers to a set of methods used in pré-processamento de dados to adjust the scale of data values to a common range, enhancing the performance of aprendizado de máquina models. By transforming features to a similar scale, these techniques help mitigate issues related to treinamento de modelos, such as convergence speed and predictive accuracy.
Existem várias técnicas de normalização comumente usadas técnicas de normalização:
- Normalização 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 é o valor original dos dados.
- Normalização 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.
- Normalização Robusta: This approach uses the median and intervalo interquartil (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-vizinhos mais próximos (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.