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Exercise

Add a model to the pipeline

You're about to take everything you've learned so far and implement it in a Pipeline that works with the real, DrivenData budget line item data you've been exploring.

Surprise! The structure of the pipeline is exactly the same as earlier in this chapter:

  • the preprocessing step uses FeatureUnion to join the results of nested pipelines that each rely on FunctionTransformer to select multiple datatypes
  • the model step stores the model object

You can then call familiar methods like .fit() and .score() on the Pipeline object pl.

Instructions
100 XP
  • Complete the 'numeric_features' transform with the following steps:
    • get_numeric_data, with the name 'selector'.
    • Imputer(), with the name 'imputer'.
  • Complete the 'text_features' transform with the following steps:
    • get_text_data, with the name 'selector'.
    • CountVectorizer(), with the name 'vectorizer'.
  • Fit the pipeline to the training data.
  • Hit 'Submit Answer' to compute the accuracy!
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