How can data normalization and scaling affect machine learning algorithms?

Tiara Williamson
Tiara WilliamsonAnswered

Data preprocessing methods like normalization and scaling are crucial for getting data into a form that machine learning algorithms can utilize. There are a number of ways these methods might influence ML algorithms.

Data normalization

This term describes the process of transforming data from a range of values to one that represents all numbers by the same number of significant figures (0 or 1). The standard method is to take the smallest value and divide it by the range (max-min). Algorithms that rely on distance measurements such as the k-nearest neighbors (k-NN) and support vector machines greatly benefit from normalization since these algorithms are sensitive to the size of the input data (SVMs). Without normalization of data in machine learning, features with big numerical values may have an outsized impact on the process, leading to mediocre model performance.

Data scaling

In contrast, data scaling involves expanding or contracting a dataset without altering its underlying distribution. Subtracting the mean and dividing by the standard deviation are common methods for this. Algorithms like linear regression and artificial neural networks are quite sensitive to the size of the input data, hence it is crucial they are scaled appropriately. By standardizing feature sizes, scaling boosts the effectiveness of popular clustering methods like k-means.

The preprocessing library in the computer language R includes useful methods for scaling data in R and normalizing such as scale() and normalize(). A dataset may be standardized with the help of the scale() function, while normalized data can be obtained with the help of the normalize() function.

  • Normalizing or scaling the data before training an ML model ensures the best possible results.

Consider how a model’s performance may be negatively impacted if, for lack of normalization, it were overly affected by characteristics with huge numerical values. If the data is not scaled, the model may not converge at all or may only do so extremely slowly.

Data pretreatment methods like normalization and scaling might have varying effects on machine learning algorithms. These methods guarantee that the data is usable for machine learning algorithms, boosting the model’s efficiency. Normalizing or scaling the data is recommended to prevent the model from being too impacted by characteristics with huge numerical values and to facilitate rapid convergence during training.

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