Technique de normalisation refers to a set of methods used in le prétraitement des données to adjust the scale of data values to a common range, enhancing the performance of apprentissage automatique models. By transforming features to a similar scale, these techniques help mitigate issues related to la formation de modèles, such as convergence speed and predictive accuracy.
Il existe plusieurs méthodes couramment utilisées Techniques de normalisation:
- Normalisation 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 est la valeur de données d'origine.
- Normalisation 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.
- Normalisation robuste : This approach uses the median and l’intervalle interquartile (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-plus proches voisins (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.