Attributes of a fitted IsotonicRegressionModel.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.regression.IsotonicRegressionModel — 4 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.regression.IsotonicRegressionModel,
org.apache.spark.ml.util.Identifiable.
Summary
Functions
Boundaries in increasing order for which predictions are known.
The JVM class these accessors belong to.
Returns the number of features the model was trained on. If unknown, returns -1.
Predict label for the given features.
Predictions associated with the boundaries at the same index, monotone because of isotonic regression.
Functions
@spec boundaries(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Boundaries in increasing order for which predictions are known.
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.
@spec class() :: String.t()
The JVM class these accessors belong to.
@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.
@spec predict(Latu.ML.Model.t(), number()) :: {: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 number, sent as a double
@spec predictions(Latu.ML.Model.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Predictions associated with the boundaries at the same index, monotone because of isotonic 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.