org.apache.spark.ml.evaluation
ClusteringEvaluator
Related Docs:
object ClusteringEvaluator
| package evaluation
org.apache.spark.ml.evaluation
An alias for getOrDefault().
An alias for getOrDefault().
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().
Copies param values from this instance to another instance for params shared by them.
Copies param values from this instance to another instance for params shared by them.
This handles default Params and explicitly set Params separately.
Default Params are copied from and to defaultParamMap, and explicitly set Params are
copied from and to paramMap.
Warning: This implicitly assumes that this Params instance and the target instance
share the same set of default Params.
the target instance, which should work with the same set of default Params as this source instance
extra params to be copied to the target's paramMap
the target instance with param values copied
Default implementation of copy with extra params.
Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.
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")
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.
Param for features column name.
Param for features column name.
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.
param for metric name in evaluation
(supports "silhouette" (default))
param for metric name in evaluation
(supports "silhouette" (default))
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.
Param for prediction column name.
Param for prediction column name.
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.
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets default values for a list of params.
Sets default values for a list of params.
Note: Java developers should use the single-parameter setDefault.
Annotating this with varargs can cause compilation failures due to a Scala compiler bug.
See SPARK-9268.
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
Sets a default value for a param.
Sets a default value for a param.
param to set the default value. Make sure that this param is initialized before this method gets called.
the default value
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.
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
:: Experimental ::
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.