Joseph K. Bradley created SPARK-19053:
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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?
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