Advanced Topics in Supervised Machine Learning

Following the tasters with scikit-learn, Keras, and PyTorch in the previous chapter, in this chapter, we will move on to more end-to-end examples. These examples are more advanced in the sense that they include more complex transformations and model types.

We'll be predicting partner choices with sklearn, where we'll implement a lot of custom transformer steps and more complicated machine learning pipelines. We'll then predict house prices in PyTorch and visualize feature and neuron importance. After that, we will perform active learning to decide customer values together with online learning in sklearn. In the well-known case of repeat offender prediction, we'll build a model without racial bias. Last, but not least, we'll forecast time series of CO₂ levels.

Online learning in this context (as opposed to internet-based learning) refers to a model update strategy that incorporates training data that comes in sequentially. This can be useful in cases where the dataset is very big (often the case with images, videos, and texts) or where it's important to keep the model up to date given the changing nature of the data.

In many of these recipes, we've shortened the description to the most salient details in order to highlight particular concepts. For the full details, please refer to the notebooks on GitHub.

In this chapter, we'll be covering the following recipes:

• Transforming data in scikit-learn
• Predicting house prices in PyTorch
• Live decisioning customer values
• Battling algorithmic bias
• Forecasting CO₂ time series