# `Latu.ML.Feature.ImputerModel`
[🔗](https://github.com/zero-one-group/latu_ml/blob/v0.4.2/lib/latu/ml/feature.ex#L0)

Attributes of a fitted `ImputerModel`.

Every accessor here is one name on the server's allowlist for
`org.apache.spark.ml.feature.ImputerModel` — 1 of them — so tab completion is that
allowlist. The server refuses anything else with `CONNECT_ML.ATTRIBUTE_NOT_ALLOWED`; these
refuse a model of the wrong class before it gets that far, and name the module that would
have taken it.

Allowed by the server but not here: `toString` is `Identifiable`'s, and tells you less than
the model's own uid. `Latu.ML.attribute/2` and `Latu.ML.attribute/3` will send any of these
names; it is what comes back that has nowhere to go.

The allowlist is inherited rather than per class: this one is the union of
`org.apache.spark.ml.feature.ImputerModel`, `org.apache.spark.ml.util.Identifiable`.

# `class`

```elixir
@spec class() :: String.t()
```

The JVM class these accessors belong to.

# `surrogate_df`

```elixir
@spec surrogate_df(Latu.ML.Model.t()) :: Latu.DataFrame.t()
```

Returns a DataFrame containing inputCols and their corresponding surrogates, which are used
to replace the missing values in the input DataFrame.

A lazy builder: the `Fetch` rides a relation, so this hands back a `Latu.DataFrame` and
nothing has run until you collect it.

---

*Consult [api-reference.md](api-reference.md) for complete listing*