Python for Finance Cookbook

How it works...

This recipe shows how easy it is to use different classifiers, as long as we want to use the default settings. In the first step, we imported the classifiers from their respective libraries.

In Steps 2 to 5, we created a special pipeline for each classifier. We combined the already established ColumnTransformer preprocessor with the corresponding classifier. Then, we fitted each pipeline to the training data and showed the performance evaluation report.

Classifiers such as XGBoost and LightGBM offer their own approaches to training models. First, you need to convert the pandas DataFrame to an acceptable data format (xgb.DMatrix, in the case of XGBoost, and lightgbm.Dataset for LightGBM), then use the train and cv functions ...