Is it true or false that you must normalize the data before numericizing it when using support vector machines?

For support vector machines (SVM), it’s essential to understand the relationship between data normalization and numericization. The statement that you must normalize the data before numericizing it is false. While normalization can be beneficial for enhancing the performance of SVM, particularly in terms of convergence speed and accuracy, it is not a prerequisite to numericization.

Numericizing data refers to converting categorical or ordinal values into a numerical format. This process can be completed independently of whether the data has been normalized or not. However, once the data is numeric, normalization becomes relevant, specifically to ensure that features contribute equally to the calculation of distances and decision boundaries. Since SVM relies on distance calculations in a high-dimensional space, unnormalized features might negatively affect the model's performance.

Normalization involves scaling the numeric data into a specific range, commonly 0 to 1 or -1 to 1. This is particularly important when the features have differing units or scales, as it avoids biases towards certain features during the optimization process to find the hyperplane that separates classes.

In summary, while normalizing data can enhance the performance of support vector machines, it is not mandatory to do so prior to the numericization process, which makes the assertion false.