Final answer:
One-hot encoding increases dimensionality by creating binary vectors for each category, leading to new features. Label encoding assigns integers to categories without adding new features, thus maintaining the dataset's dimensionality.
Step-by-step explanation:
When discussing one-hot encoding and label encoding, we are looking at two common techniques used to convert categorical data into a form that can be provided to machine learning algorithms. With one-hot encoding, each unique category value is transformed into a binary vector with one bit for each unique value and a marking of 1 in the position of the assigned category, while all other bits are set to 0. This approach inevitably increases the dimensionality of the dataset because it creates a new feature for each unique category value. Conversely, label encoding assigns a unique integer to each category value. Since label encoding transforms the categorical data into a single new feature irrespective of the number of categories, it does not increase the dimensionality of the data set.