ml_gaussian_mixture {sparklyr} | R Documentation |
This class performs expectation maximization for multivariate Gaussian Mixture Models (GMMs). A GMM represents a composite distribution of independent Gaussian distributions with associated "mixing" weights specifying each's contribution to the composite. Given a set of sample points, this class will maximize the log-likelihood for a mixture of k Gaussians, iterating until the log-likelihood changes by less than tol
, or until it has reached the max number of iterations. While this process is generally guaranteed to converge, it is not guaranteed to find a global optimum.
ml_gaussian_mixture(x, formula = NULL, k = 2L, max_iter = 100L, tol = 0.01, seed = NULL, features_col = "features", prediction_col = "prediction", probability_col = "probability", uid = random_string("gaussian_mixture_"), ...)
x |
A |
formula |
Used when |
k |
The number of clusters to create |
max_iter |
The maximum number of iterations to use. |
tol |
Param for the convergence tolerance for iterative algorithms. |
seed |
A random seed. Set this value if you need your results to be reproducible across repeated calls. |
features_col |
Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by |
prediction_col |
Prediction column name. |
probability_col |
Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities. |
uid |
A character string used to uniquely identify the ML estimator. |
... |
Optional arguments; currently unused. |
The object returned depends on the class of x
.
spark_connection
: When x
is a spark_connection
, the function returns an instance of a ml_estimator
object. The object contains a pointer to
a Spark Estimator
object and can be used to compose
Pipeline
objects.
ml_pipeline
: When x
is a ml_pipeline
, the function returns a ml_pipeline
with
the clustering estimator appended to the pipeline.
tbl_spark
: When x
is a tbl_spark
, an estimator is constructed then
immediately fit with the input tbl_spark
, returning a clustering model.
tbl_spark
, with formula
or features
specified: When formula
is specified, the input tbl_spark
is first transformed using a
RFormula
transformer before being fit by
the estimator. The object returned in this case is a ml_model
which is a
wrapper of a ml_pipeline_model
. This signature does not apply to ml_lda()
.
See http://spark.apache.org/docs/latest/ml-clustering.html for more information on the set of clustering algorithms.
Other ml clustering algorithms: ml_bisecting_kmeans
,
ml_kmeans
, ml_lda