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https://issues.apache.org/jira/browse/SPARK-27867?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-27867:
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Assignee: (was: Apache Spark)
> RegressionEvaluator cache lastest RegressionMetrics to avoid duplicated
> computation
> -----------------------------------------------------------------------------------
>
> Key: SPARK-27867
> URL: https://issues.apache.org/jira/browse/SPARK-27867
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 3.0.0
> Reporter: zhengruifeng
> Priority: Major
>
> In most cases, given a model, we have to obtain multi metrics of it.
> For examples, a regression model, we may need to obtain the R2, MAE and MSE.
> However, current design of `Evaluator` do not support computing multi metrics
> at once.
> In practice, we usually use RegressionEvaluator like this:
> {code:java}
> val evaluator = new RegressionEvaluator()
> val r2 = evaluator.setMetricName("r2").evaluate(df)
> val mae = evaluator.setMetricName("mae").evaluate(df)
> val mse = evaluator.setMetricName("mse").evaluate(df){code}
>
> However, current impl of RegressionEvaluator needs one pass of the whole
> input dataset to compute one metric. So, above example needs 3 passes.
> This can be optimized since in \{RegressionMetrics} all metrics can be
> computed at once.
> If we cache the lastest inputs, and then if the next evaluate call keep the
> inputs (except the metricName), then we can directly obtain the metric from
> the internal intermediate summary.
>
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