Xiangrui Meng created SPARK-14831:
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Summary: Make ML APIs in SparkR consistent
Key: SPARK-14831
URL: https://issues.apache.org/jira/browse/SPARK-14831
Project: Spark
Issue Type: Improvement
Components: ML, SparkR
Affects Versions: 2.0.0
Reporter: Xiangrui Meng
Assignee: Xiangrui Meng
Priority: Critical
In current master, we have 4 ML methods in SparkR:
{code:none}
glm(formula, family, data, ...)
kmeans(data, centers, ...)
naiveBayes(formula, data, ...)
survreg(formula, data, ...)
{code}
We tried to keep the signatures similar to existing ones in R. However, if we
put them together, they are not consistent. One example is k-means, which
doesn't accept a formula. Instead of looking at each method independently, we
might want to update the signature of kmeans to
{code:none}
kmeans(formula, data, centers, ...)
{code}
We can also discuss possible global changes here. For example, `glm` puts
`family` before `data` while `kmeans` puts `centers` after `data`. This is not
consistent. And logically, the formula doesn't mean anything without
associating with a DataFrame. So it makes more sense to me to have the
following signature:
{code:none}
algorithm(data, formula, [required params], [optional params])
{code}
If we make this change, we might want to avoid name collisions because they
have different signature. We can use `ml.kmeans`, 'ml.glm`, etc.
Sorry for discussing API changes in the last minute. But I think it would be
better to have consistent signatures in SparkR.
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