Description: Feature scaling is the process of normalizing or standardizing the range of independent variables or features of the data, which is essential for many machine learning algorithms. This process ensures that all features contribute equally to the analysis, preventing variables with wider ranges from dominating the model. There are different scaling methods, such as normalization, which adjusts values to a specific range, and standardization, which transforms data to have a mean of zero and a standard deviation of one. Feature scaling is particularly relevant in algorithms that use distances, such as K-nearest neighbors (KNN) and support vector machines (SVM), where the scale of features can significantly influence model performance. Additionally, in the context of neural networks, proper scaling can accelerate the convergence process during training. In summary, feature scaling is a critical step in data preprocessing that enhances the effectiveness and accuracy of machine learning models.
