Understanding Normalization in Machine Learning Model Training

What is Normalization?

Within the field of machine learning, normalization is essential as it plays a vital role in the quality of model training performance. Normalization is a technique employed to bring data into a standard format, ensuring fair treatment of all features and optimising the learning process. Essentially, the method scales the input features to a similar range. This in turn ensures that each feature contributes equally to the learning process without one feature dominating over the others due to its larger scale.

For example, we have one feature that ranges from 0 to 1000 and another that ranges from 0 to 1. Without normalizing, the first feature would dominate the learning process. But through normalizing techniques, you ensure that each feature is treated equally by the learning algorithm.

The Need for Normalization

Datasets in real-world applications are rarely pristine and uniform. Features may span different ranges, have varying scales, or exhibit disparate units. For instance, consider a dataset comprising both temperature readings in Celsius and financial transactions in thousands of dollars. Without normalization, features with larger scales or ranges might dominate the learning process, overshadowing the contributions of other features. Consequently, the model’s ability to generalize to unseen data diminishes, leading to suboptimal performance.

The following are some of the benefits of using normalization techniques:

Improved Convergence: Normalizing input features facilitates faster convergence during model training, as it prevents oscillations and overshooting caused by disparate feature scales.
Enhanced Model Performance: By treating all features equally, normalization enables the model to discern meaningful patterns from each feature, thereby improving its predictive accuracy and generalization ability.
Robustness to Scaling: Normalization renders machine learning models robust to variations in feature scales, making them less susceptible to biases introduced by arbitrary units or scales.

Types of Normalization

Within this section, we cover three common techniques. Before diving into them, it is important to remember some good practices when implementing normalization.

Normalize training and test data separately to ensure the integrity of the evaluation process and prevent data leakage.
Consider the nature of data as normalizing may not always be necessary or suitable for certain types of data.
Monitor performance to assess the impact of normalization to the end outcome.

Min-Max Normalization

Also known as scaling, min-max scales the features to a fixed range, typically between 0 and 1. It preserves the original distribution of the data while ensuring all features are within the same scale.

Z-score Normalization (Standardization)

This method standardizes the data by transforming it to have a mean of 0 and a standard deviation of 1. By centering the data around zero and scaling it by the standard deviation, z-score is effective in handling outliers and making features comparable.

Scaling to Unit Length

Commonly used in vector-based algorithms, this technique scales the vector of features so that its magnitude equals 1. It is particularly beneficial in scenarios where the magnitude of features is more relevant than their absolute values.