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

Attributes of a fitted `AFTSurvivalRegressionModel`.

Every accessor here is one name on the server's allowlist for
`org.apache.spark.ml.regression.AFTSurvivalRegressionModel` — 6 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.PredictionModel`,
`org.apache.spark.ml.regression.AFTSurvivalRegressionModel`,
`org.apache.spark.ml.util.Identifiable`.

# `class`

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

The JVM class these accessors belong to.

# `coefficients`

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

Model coefficients.

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.

# `intercept`

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

Model intercept.

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.

# `num_features`

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

Returns the number of features the model was trained on. If unknown, returns -1.

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.

# `predict`

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

Predict label for the given features.

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.

## Arguments

  * `value` — a Spark `Vector`: an `Nx.Tensor` or a `Latu.ML.SparseVector`

# `predict_quantiles`

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

Predicted Quantiles.

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.

## Arguments

  * `features` — a Spark `Vector`: an `Nx.Tensor` or a `Latu.ML.SparseVector`

# `scale`

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

Model scale parameter.

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*