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

Attributes of a fitted `LogisticRegressionModel`.

Every accessor here is one name on the server's allowlist for
`org.apache.spark.ml.classification.LogisticRegressionModel` — 11 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; `evaluate` answers with a training summary, which has no struct yet.
`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.classification.ClassificationModel`,
`org.apache.spark.ml.classification.LogisticRegressionModel`,
`org.apache.spark.ml.classification.ProbabilisticClassificationModel`,
`org.apache.spark.ml.util.HasTrainingSummary`, `org.apache.spark.ml.util.Identifiable`.

# `class`

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

The JVM class these accessors belong to.

# `coefficient_matrix`

```elixir
@spec coefficient_matrix(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.

# `coefficients`

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

Model coefficients of binomial logistic regression. An exception is thrown in the case of
multinomial logistic regression.

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.

# `has_summary`

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

Indicates whether a training summary exists for this model instance.

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 of binomial logistic regression. An exception is thrown in the case of
multinomial logistic regression.

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_vector`

```elixir
@spec intercept_vector(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_classes`

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

Number of classes (values which the label can take).

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_probability`

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

Predict the probability of each class given the 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_raw`

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

Raw prediction for each possible label.

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`

# `summary`

```elixir
@spec summary(Latu.ML.Model.t()) :: Latu.ML.Summary.t()
```

Gets summary of the model trained on the training set. An exception is thrown if no summary
exists.

A lazy builder: Spark reaches a summary through the model that owns it, so the reference is
composed here and nothing is sent. `Latu.ML.summary/1` is the same thing without the class
check.

---

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