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

Attributes of a fitted `StandardScalerModel`.

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

# `class`

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

The JVM class these accessors belong to.

# `mean`

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

Mean of the StandardScalerModel.

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.

# `std`

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

Standard deviation of the StandardScalerModel.

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.

---

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