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Type:
Sub-task
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Status: Resolved
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Priority:
Minor
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Resolution: Incomplete
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Affects Version/s: None
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Fix Version/s: None
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Component/s: ML
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Labels:
The current implementation of Estimator does not support warm-start fitting, i.e. estimator.fit(data, params, partialModel). But first we need to add warm-start for all ML estimators. This is an umbrella JIRA to add support for the warm-start estimator.
Treat model as a special parameter, passing it through ParamMap. e.g. val partialModel: Param[Option[M]] = new Param(...). In the case of model existing, we use it to warm-start, else we start the training process from the beginning.
- contains
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SPARK-7852 Set the initial weights based on the previous when GLMs are run with multiple regParams
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- Resolved
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SPARK-13856 Support initialModel in ALS
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- Resolved
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SPARK-15785 Add initialModel param to Gaussian Mixture Model (GMM) in spark.ml
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- Resolved
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SPARK-21386 ML LinearRegression supports warm start from user provided initial model
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- Resolved
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SPARK-10780 Set initialModel in KMeans in Pipelines API
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- Resolved
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SPARK-13025 Allow user to specify the initial model when training LogisticRegression
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- Resolved
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- is duplicated by
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SPARK-13026 Umbrella: Allow user to specify initial model when training
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- Resolved
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- is related to
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SPARK-20082 Incremental update of LDA model, by adding initialModel as start point
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- In Progress
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- relates to
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SPARK-12098 Cross validator with multi-arm bandit search
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- Closed
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