Attributes of a GeneralizedLinearRegressionSummary — what an estimator recorded while it fitted. Latu.ML.summary/1 is what hands you one.
Every accessor here is one name on the server's allowlist for
org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary — 12 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.
Every allowlisted attribute of this class has a function here.
The allowlist is inherited rather than per class: this one is the union of
org.apache.spark.ml.regression.GeneralizedLinearRegressionSummary.
Summary
Functions
Akaike's "An Information Criterion"(AIC) for the fitted model.
The JVM class these accessors belong to.
Degrees of freedom.
The deviance for the fitted model.
The dispersion of the fitted model. It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise estimated by the residual Pearson's Chi-Squared statistic (which is defined as sum of the squares of the Pearson residuals) divided by the residual degrees of freedom.
The deviance for the null model.
Number of instances in DataFrame predictions.
Field in predictions which gives the predicted value of each instance. This is set to a
new column name if the original model's predictionCol is not set.
Predictions output by the model's transform method.
The numeric rank of the fitted linear model.
The residual degrees of freedom.
The residual degrees of freedom for the null model.
Get the residuals of the fitted model by type.
Functions
@spec aic(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Akaike's "An Information Criterion"(AIC) for the fitted model.
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 degrees_of_freedom(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Degrees of freedom.
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 deviance(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
The deviance for the fitted model.
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 dispersion(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
The dispersion of the fitted model. It is taken as 1.0 for the "binomial" and "poisson" families, and otherwise estimated by the residual Pearson's Chi-Squared statistic (which is defined as sum of the squares of the Pearson residuals) divided by the residual degrees of freedom.
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 null_deviance(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
The deviance for the null model.
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 num_instances(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Number of instances in DataFrame predictions.
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 prediction_col(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
Field in predictions which gives the predicted value of each instance. This is set to a
new column name if the original model's predictionCol is not set.
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 predictions(Latu.ML.Summary.t()) :: Latu.DataFrame.t()
Predictions output by the model's transform method.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
@spec rank(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
The numeric rank of the fitted linear model.
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 residual_degree_of_freedom(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
The residual degrees of freedom.
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 residual_degree_of_freedom_null(Latu.ML.Summary.t()) :: {:ok, term()} | {:error, Latu.Error.t()}
The residual degrees of freedom for the null model.
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 residuals(Latu.ML.Summary.t(), String.t() | atom()) :: Latu.DataFrame.t()
Get the residuals of the fitted model by type.
A lazy builder: the Fetch rides a relation, so this hands back a Latu.DataFrame and
nothing has run until you collect it.
Arguments
residuals_type— a string, or an atom