Update Dec/2020: Fixed small typo in API example.Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.
TRANSTYPE 5 HOW TO
How to work through a real dataset with mixed data types and use the ColumnTransformer to apply different transforms to categorical and numerical data columns.How to define, fit, and use the ColumnTransformer to selectively apply data transforms to columns.The challenge of using data transformations with datasets that have mixed data types.In this tutorial, you will discover how to use the ColumnTransformer to selectively apply data transforms to columns in a dataset with mixed data types.Īfter completing this tutorial, you will know: Thankfully, the scikit-learn Python machine learning library provides the ColumnTransformer that allows you to selectively apply data transforms to different columns in your dataset. It can be challenging when you have a dataset with mixed types and you want to selectively apply data transforms to some, but not all, input features. This is required to ensure that you best expose the structure of your predictive modeling problem to the learning algorithms.Īpplying data transforms like scaling or encoding categorical variables is straightforward when all input variables are the same type. You must prepare your raw data using data transforms prior to fitting a machine learning model.
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