Standardized code to train and evaluate machine learning models

There are two main ingredients while training a machine learning model – data and the model itself. Therefore, to standardize the pipeline, we defined three configuration classes (FeatureConfig, MissingValueConfig, and ModelConfig) and another wrapper class (MLForecast) over scikit-learn-style estimators (.fit - .predict) to make the process smooth. Let’s look at each of them.

Notebook alert

To follow along with the code, use the 01-Forecasting with ML.ipynb notebook in the chapter08 folder and the code in the src folder.

FeatureConfig

FeatureConfig is a Python dataclass that defines a few key attributes and functions that are necessary while processing the data. For instance, continuous, categorical, and Boolean columns need separate kinds of preprocessing before being fed into the machine learning model. Let’s see what FeatureConfig holds:

• date: A mandatory column that sets the...