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yuhao yang commented on SPARK-19053: ------------------------------------ Plan for further development: 1. Initial API and function parity with ML Evaluators. (This PR) 2. Python API. 3. Function parity with MLlib Metrics. 4. Add requested enhancements like including weight, add per-row metrics, add ranking metrics. 5. Reorganize classification Metrics hierarchy, so Binary Classification Metrics can support metrics in MultiClassMetrics (accuracy, recall etc.). 6. Possibly to be used in training summary. > Supporting multiple evaluation metrics in DataFrame-based API: discussion > ------------------------------------------------------------------------- > > Key: SPARK-19053 > URL: https://issues.apache.org/jira/browse/SPARK-19053 > Project: Spark > Issue Type: Brainstorming > Components: ML > Reporter: Joseph K. Bradley > > This JIRA is to discuss supporting the computation of multiple evaluation > metrics efficiently in the DataFrame-based API for MLlib. > In the RDD-based API, RegressionMetrics and other *Metrics classes support > efficient computation of multiple metrics. > In the DataFrame-based API, there are a few options: > * model/result summaries (e.g., LogisticRegressionSummary): These currently > provide the desired functionality, but they require a model and do not let > users compute metrics manually from DataFrames of predictions and true labels. > * Evaluator classes (e.g., RegressionEvaluator): These only support computing > a single metric in one pass over the data, but they do not require a model. > * new class analogous to Metrics: We could introduce a class analogous to > Metrics. Model/result summaries could use this internally as a replacement > for spark.mllib Metrics classes, or they could (maybe) inherit from these > classes. > Thoughts? -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org