Preprocessing

Development build. This page describes main, not a released package. The latest published Lodestar.Preprocessing is 0.1.0 — read its documentation.

Preprocessing

Namespaces

• Feature scaling — Lodestar.Preprocessing — Four scalers, at sklearn.preprocessing parity: StandardScaler centres on the mean and scales to unit variance, MinMaxScaler maps onto a fixed range, MaxAbsScaler divides by the largest absolute value without ever subtracting, and RobustScaler centres on the median and scales by an interpercentile range.
• Splitting — Lodestar.Preprocessing — One entry point, Splitters: it cuts the rows into cross-validation folds or into a single train and test split, at sklearn.model_selection parity wherever the reference is deterministic.
• Encoding and imputation — Lodestar.Preprocessing — Two encoders and an imputer, at sklearn.preprocessing and sklearn.impute parity, over row-major spans rather than an IDataView: Encoders.OneHot gives each category a column, Encoders.Ordinal gives it a code, and SimpleImputer fills what is missing.