org.apache.spark.ml.evaluation
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
param for distance measure to be used in evaluation
(supports "squaredEuclidean"
(default), "cosine"
)
param for distance measure to be used in evaluation
(supports "squaredEuclidean"
(default), "cosine"
)
Param for features column name.
Param for features column name.
param for metric name in evaluation
(supports "silhouette"
(default))
param for metric name in evaluation
(supports "silhouette"
(default))
Param for prediction column name.
Param for prediction column name.
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
Creates a copy of this instance with the same UID and some extra params.
Creates a copy of this instance with the same UID and some extra params.
Subclasses should implement this method and set the return type properly.
See defaultCopy()
.
Evaluates model output and returns a scalar metric.
Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.
a dataset that contains labels/observations and predictions.
metric
Evaluates model output and returns a scalar metric.
Evaluates model output and returns a scalar metric. The value of isLargerBetter specifies whether larger values are better.
a dataset that contains labels/observations and predictions.
parameter map that specifies the input columns and output metrics
metric
Explains a param.
Explains a param.
input param, must belong to this instance.
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
Explains all params of this instance.
Explains all params of this instance. See explainParam()
.
extractParamMap
with no extra values.
extractParamMap
with no extra values.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
Gets the default value of a parameter.
Gets the default value of a parameter.
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
Gets a param by its name.
Gets a param by its name.
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
Indicates whether the metric returned by evaluate
should be maximized (true, default)
or minimized (false).
Indicates whether the metric returned by evaluate
should be maximized (true, default)
or minimized (false).
A given evaluator may support multiple metrics which may be maximized or minimized.
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
Saves this ML instance to the input path, a shortcut of write.save(path)
.
Saves this ML instance to the input path, a shortcut of write.save(path)
.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
Returns an MLWriter
instance for this ML instance.
Returns an MLWriter
instance for this ML instance.
Evaluator for clustering results. The metric computes the Silhouette measure using the specified distance measure.
The Silhouette is a measure for the validation of the consistency within clusters. It ranges between 1 and -1, where a value close to 1 means that the points in a cluster are close to the other points in the same cluster and far from the points of the other clusters.