Normalization in preprocessing typically involves which of the following processes?

Normalization in the context of preprocessing primarily refers to the techniques used to scale features so that they contribute equally to model training. This approach is crucial in scenarios where different features have vastly different ranges, as it helps in improving the performance of machine learning algorithms that are sensitive to the scale of data.

Feature scaling and transformation, which include normalizing data to a scale of 0 to 1 or standardizing it to have a mean of zero and a standard deviation of one, are key processes involved in normalization. Both techniques assist in ensuring that models converge more quickly and perform better by treating all features uniformly.

The other options refer to different preprocessing techniques. Encoding categorical variables converts categorical data into numerical formats suitable for machine learning algorithms. Stemming and lemmatization relate to text preprocessing in natural language processing, focusing on reducing words to their base or root forms, while removing accents is a text normalization process but separate from feature scaling. Dimensionality reduction aims to reduce the number of features in a dataset without losing significant information, which is different from normalization.

Thus, feature scaling and transformation are integral to the normalization process, ensuring that all input data are on a similar scale for optimal model performance.