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- Profiling: Use Profile Snap from ML Analytics Snap Pack to get statistics of this dataset.
- Data Preparation: Perform data preparation on this dataset using Snaps in ML Data Preparation Snap Pack.
- Cross Validation: Use Cross Validator (Classification) Snap from ML Core Snap Pack to perform 10-fold cross validation on various Machine Learning algorithms. The result will let us know the accuracy of each algorithm in the success rate prediction.
We are going to build 4 pipelines: Profiling, Data Preparation, and 2 pipelines for Cross Validation with various algorithms. Each of these pipelines is described in the Pipelines section below.
Pipelines
Profiling
In order to get useful statistics, we need to transform the data a little bit.
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The $algorithm from CSV Generator will be passed into the child pipeline as pipeline parameter. For each algorithm, a child pipeline instance will be spawned and executed. You can execute multiple child pipelines at the same time by adjusting Pool Size. The output of the child pipeline will be the output of this Snap. Moreover, the input document of the Pipeline Execute Snap will be added to the output as $original.
The Mapper Snap is used to extract the algorithm name and accuracy from the output of the Pipeline Execute Snap.
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