Missing values

Missing data is handled explicitly, never silently: a small set of imputation parameters covers the common case, and a single missing_policy gives finer control when you need it.

Imputation parameters

Parameter

Meaning

Default

numerical_imputation

Strategy for numerical columns. None disables it.

"median"

categorical_imputation

Strategy for categorical columns. None disables it.

"most_frequent"

add_missing_indicator

Adds a binary indicator column marking where a value was missing.

False

 
from pretab import Preprocessor

pre = Preprocessor(
    numerical_imputation="median",
    categorical_imputation="most_frequent",
    add_missing_indicator=True,
)

Note

Disabling imputation lets NaN reach the transformer directly. Scalers, splines, and the feature maps (rbf, relu, sigmoid, tanh) tolerate it, so an affected row’s output is itself undefined; finite-only methods (PLE, numeric binning, periodic encoding, Fourier features, rff, nystroem) raise a typed error instead, since expanding an undefined value has no meaning for them. add_missing_indicator=True still works with imputation disabled: it routes through the same __missing branch missing_policy="separate_state" uses below.

Important

Imputers fit their fill values only on the data passed to fit, and reuse those values unchanged at transform. PreTab never drops a row for missing values: every input row produces an output row.

The missing_policy control

For finer control, missing_policy selects one of five behaviours for the whole preprocessor.

Policy

Behaviour

"error"

Reject any missing value at fit and transform.

"propagate"

Pass missing values through to the transformer unchanged.

"impute"

Fill using the imputation parameters above.

"impute_with_indicator"

Impute and add a missing indicator column.

"separate_state"

Impute the basis, and add a dedicated __missing column that does not activate the ordinary basis.

 
pre = Preprocessor(missing_policy="separate_state")

Separate state

"separate_state" is the most expressive option: it keeps the imputed value flowing into the normal basis while emitting a parallel __missing indicator a model can weight on its own, so it can learn a distinct effect for “missing” without corrupting the shape learned on observed values.

Tip

Reach for "separate_state" (or add_missing_indicator=True) when missingness itself carries signal. Reach for plain "impute" when a value is missing purely at random, "error" to catch missingness as a data bug, and "propagate" (with imputation disabled) to handle it upstream yourself.

Where to go next