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From "Joseph K. Bradley (JIRA)" <>
Subject [jira] [Commented] (SPARK-19053) Supporting multiple evaluation metrics in DataFrame-based API: discussion
Date Wed, 18 Jan 2017 20:54:27 GMT


Joseph K. Bradley commented on SPARK-19053:

After thinking about this more and hearing your thoughts, my top pick would be to define Metrics
classes similar to the spark.mllib ones, with the current model summaries inheriting from
these metrics classes.  (We should sketch out APIs before implementation, though, to make
sure this setup will not break any APIs or force awkward APIs.)

Metrics classes could offer both aggregate and per-row metrics.

> Supporting multiple evaluation metrics in DataFrame-based API: discussion
> -------------------------------------------------------------------------
>                 Key: SPARK-19053
>                 URL:
>             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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