Description: Input feature normalization is a fundamental process in data preprocessing, which involves scaling input features to ensure they are on the same scale during model training. This process is crucial because features can have different units and ranges, which can lead to some features dominating others in the training process. Normalization helps improve the convergence of optimization algorithms and increases model accuracy. There are different normalization methods, such as Min-Max normalization, which scales data to a specific range, and Z-score normalization, which transforms data to have a mean of zero and a standard deviation of one. The choice of normalization method depends on the type of data and the algorithm to be used. In summary, input feature normalization is an essential step to ensure that machine learning models operate effectively and efficiently, allowing for better interpretation and analysis of data.
