Normalization Technique refers to a set of methods used in data preprocessing to adjust the scale of data values to a common range, enhancing the performance of machine learning models. By transforming features to a similar scale, these techniques help mitigate issues related to model training, such as convergence speed and predictive accuracy.
There are several commonly used normalization techniques:
- Min-Max Normalization: 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 is the original data value.
- Z-score Normalization: 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.
- Robust Normalization: This approach uses the median and interquartile range (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-nearest neighbors (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.