# ml_clustering_evaluator

##### Spark ML - Clustering Evaluator

Evaluator for clustering results. The metric computes the Silhouette measure using the squared Euclidean distance. 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.

##### Usage

```
ml_clustering_evaluator(
x,
features_col = "features",
prediction_col = "prediction",
metric_name = "silhouette",
uid = random_string("clustering_evaluator_"),
...
)
```

##### Arguments

- x
A

`spark_connection`

object or a`tbl_spark`

containing label and prediction columns. The latter should be the output of`sdf_predict`

.- features_col
Name of features column.

- prediction_col
Name of the prediction column.

- metric_name
The performance metric. Currently supports "silhouette".

- uid
A character string used to uniquely identify the ML estimator.

- ...
Optional arguments; currently unused.

##### Value

The calculated performance metric

##### Examples

```
# NOT RUN {
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
partitions <- iris_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
iris_training <- partitions$training
iris_test <- partitions$test
formula <- Species ~ .
# Train the models
kmeans_model <- ml_kmeans(iris_training, formula = formula)
b_kmeans_model <- ml_bisecting_kmeans(iris_training, formula = formula)
gmm_model <- ml_gaussian_mixture(iris_training, formula = formula)
# Predict
pred_kmeans <- ml_predict(kmeans_model, iris_test)
pred_b_kmeans <- ml_predict(b_kmeans_model, iris_test)
pred_gmm <- ml_predict(gmm_model, iris_test)
# Evaluate
ml_clustering_evaluator(pred_kmeans)
ml_clustering_evaluator(pred_b_kmeans)
ml_clustering_evaluator(pred_gmm)
# }
```

*Documentation reproduced from package sparklyr, version 1.5.0, License: Apache License 2.0 | file LICENSE*