Description: Feature space normalization is a fundamental process in data preprocessing that aims to scale the features of a dataset so that all contribute equally to the machine learning model. This process is crucial because features can have different units of measurement and ranges, which can lead to some features dominating the model training process. By normalizing, the values of the features are adjusted to a common range, typically between 0 and 1, or given a mean of 0 and a standard deviation of 1. This not only improves the convergence of optimization algorithms but can also enhance the model’s accuracy. Normalization is especially important in algorithms that rely on distance, such as k-nearest neighbors (k-NN) and support vector machines (SVM), where the scale of the features can significantly influence performance. In summary, feature space normalization is an essential technique that ensures each feature has a balanced impact on the model, thereby facilitating more effective and efficient learning.
