Encoding: creating feature matrices

Encoding or vectorizing creates numerical features from the data, converting dataframes, strings, dates… Different encoders are suited for different types of data.

Encoding open-ended entries and dirty categories

String columns can be seen categories for statistical analysis, but standard tools to represent categories fail if these strings are not normalized into a small number of well-identified form, if they have typos, or if there are too many categories.

Skrub provides encoders that represent well open-ended strings or dirty categories, eg to replace OneHotEncoder:

• GapEncoder: infers latent categories and represent the data on these. Very interpretable, sometimes slow

• MinHashEncoder: a very scalable encoding of strings capturing their similarities. Particularly useful on large databases and well suited for learners such as trees (boosted trees or random forests)

• SimilarityEncoder: a simple encoder that works by representing strings similarities with all the different categories in the data. Useful when there are a small number of categories, but we still want to capture the links between them (eg: “west”, “north”, “north-west”)

References::

For a detailed description of the problem of encoding dirty categorical data, see Similarity encoding for learning with dirty categorical variables [1] and Encoding high-cardinality string categorical variables [2].

[1]

Patricio Cerda, Gaël Varoquaux. Encoding high-cardinality string categorical variables. 2020. IEEE Transactions on Knowledge & Data Engineering.

[2]

Patricio Cerda, Gaël Varoquaux, Balázs Kégl. Similarity encoding for learning with dirty categorical variables. 2018. Machine Learning journal, Springer.

Encoding dates

The DatetimeEncoder encodes date and time: it represent them as time in seconds since a fixed date, but also added features useful to capture regularities: week of the day, month of the year…