Normalization in Machine Learning
Normalization is a data preparation technique frequently used in machine learning to transform features to be on a similar scale, improving the performance and training stability of the model. This process involves changing the values of numeric columns in the dataset to use a common scale, which is particularly useful when the features of machine learning models have different ranges.
The goal of normalization is to ensure that features with different scales do not unduly influence the training process, especially when employing techniques such as k-nearest neighbors and artificial neural networks that do not make assumptions about the distribution of the data.
Common normalization techniques include scaling to a range, clipping, log scaling, and z-score. Scaling to a range involves transforming the data to a specific range, while clipping caps all feature values above or below a certain value to a fixed value. Z-score, on the other hand, squeezes raw values into a range from roughly -1 to +4, making it a suitable choice when the distribution of the data is not Gaussian.
In summary, normalization is a crucial step in preparing data for machine learning, ensuring that the model is trained effectively and accurately by addressing the issue of varying feature scales. It is not always necessary for all datasets, but it is essential when the features of the machine learning model have different ranges. Normalization helps to avoid raw data and various problems of datasets by creating new values and maintaining general distribution and ratio in the data, ultimately improving the performance and accuracy of machine learning models.
