What is Z-score normalization

Machine learning is a field that involves the use of algorithms and statistical models to enable computer systems to perform and improve various tasks, from decision-making, speech recognition, to image recognition. One of the fundamental processes that machine learning practitioners face is data preprocessing, which includes data cleaning, feature scaling, and normalization. In this article, we will discuss what z-score normalization is in machine learning and why it is crucial.

Z-score normalization (also known as standardization) is a statistical technique commonly used in machine learning to rescale numerical data. It involves transforming raw data points to standard units so that they have a mean of zero and a standard deviation of one. This normalization technique allows different features to be comparable, removes the scales’ effect on classification models, and increases the accuracy of various machine learning algorithms for the following reasons.

Firstly, z-score normalization improves the interpretability of the dataset by making it easier to understand the impact of each feature. Data points with very different ranges and units of measurement can skew the analysis of a machine learning model. For example, consider a dataset consisting of two features, height (cm) and income (dollars) in which one feature has a wider range than the other. With such a dataset, the machine learning model may allocate more weight to the feature with a larger range, leading to erroneous results. However, if all features are normalized to a standard unit, the algorithm will process the data equally.

Furthermore, standardization is an efficient way to reduce the impact of outliers on the performance of machine learning models. Outliers are data points that typically lie far away from the rest of the data and can drastically affect the mean and standard deviation of a dataset. By normalizing data to a z-score, outliers become less prevalent, making the model more effective in generating accurate predictions.

Finally, z-score normalization also improves the performance of models that use gradient-based optimizers like stochastic gradient descent, where the weights’ values are updated with each iteration of the algorithm. The standardization eliminates vanishing and exploding gradients along with a quicker convergence rate.

While z-score normalization is generally useful, it may not always be necessary for machine learning applications. For example, when using some algorithms like decision trees, we do not require data to have a standard mean of zero and a standard deviation of one. Additionally, standardization may reduce the interpretability of the data to signify real-world units relevant to the data types.

In conclusion, z-score normalization is a vital statistical technique in machine learning that rescales data to have a mean of zero and a standard deviation of one. This technique is an efficient way to make features interpretable, reduce the impact of outliers on performance, and improve models that use gradient-based optimizers. While standardization is not necessary in all machine learning applications, it remains a critical preprocessing step for various models.