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I got this aswer from chatGPT (TLDR it acts as a default placeholder):
The pipeline variable is designed to define a sequence of data processing and model training steps that should be executed in order. The reason for including a LinearRegression model in the pipeline is to establish a default model for the pipeline. This setup allows you to specify a generic sequence of steps without committing to a specific model until you run GridSearchCV.
Here’s the breakdown of its purpose:
Default Model Setup: Initially, the pipeline is set up with a default model (LinearRegression in this case). This setup is necessary because a pipeline needs to end with an estimator, and LinearRegression serves as a placeholder until the best model is identified through GridSearchCV.
Flexibility: By defining the pipeline with a default model, you maintain flexibility. When GridSearchCV is run with the search_space, it dynamically replaces the LinearRegression model with each model specified in the search_space. This way, you can test multiple models using the same preprocessing steps without rewriting the pipeline.
Consistency in Preprocessing: The pipeline ensures that the same preprocessing steps are applied consistently across all models tested in the grid search. Whether the final model ends up being LinearRegression, Ridge, or Lasso, each will receive data that has been processed in the same way.
Simplification: Using a pipeline simplifies the code and workflow. It encapsulates all steps into a single object, making the code cleaner and reducing the risk of errors, such as applying transformations to the training set but forgetting to do the same to the test set.
In summary, the LinearRegression in the pipeline serves as a default placeholder, ensuring the pipeline is complete and executable even before the best model is selected through GridSearchCV. Once GridSearchCV is executed, it finds the best model and its parameters, which could be LinearRegression or any other model specified in the search_space.
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