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

Attributes of a fitted `VectorIndexerModel`.

Every accessor here is one name on the server's allowlist for
`org.apache.spark.ml.feature.VectorIndexerModel` — 2 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.VectorIndexerModel`, `org.apache.spark.ml.util.Identifiable`.

# `category_maps_df`

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

Spark's `categoryMapsDF`. PySpark exposes no attribute under that name, so there is no
description of it to quote here.

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

# `class`

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

The JVM class these accessors belong to.

# `num_features`

```elixir
@spec num_features(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
```

Number of features, i.e., length of Vectors which this transforms.

An action: it reaches the server. A `Vector` or `Matrix` comes back as an `Nx.Tensor`, or a
`Latu.ML.SparseVector` where densifying would be this package's decision rather than yours.

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