Python for Finance Cookbook

How it works...

In the first step, we imported the required libraries. Then, we extracted the classifier and the ColumnTransformer preprocessor from the pipeline. In this recipe, we worked with a vanilla Random Forest classifier, without any hyperparameter tuning. The reason for this is that it gives a decent performance and, additionally, the computation time is much lower in comparison to the tuned one (with a higher number of estimators, and so on).

Please see the accompanying Notebook in the book's GitHub repository to see how to extract the best pipeline from a fitted grid search, or how to assign the best hyperparameter values manually.

In Step 3, we extracted the column names from the ColumnTransformer preprocessor. It is important ...