Classification – Decision Tree Learning

In the previous chapter, we discussed different types of machine learning, including supervised classification tasks; in this chapter, we will build our first Swift application for this. We will discuss main components of machine learning development stack, and will also exercise in data generation, exploratory analysis, preprocessing, and models training and evaluation in Python. After this, we will transfer our model to Swift. We will also discuss a specific class of supervised learning algorithms—decision tree learning and its extension: random forest.

The following topics are waiting for us in this chapter:

• Machine learning software development stack
• Python toolbox for machine learning: IPython, SciPy, scikit-learn
• Dataset generation and exploratory analysis
• Data preprocessing
• Decision tree learning and random forest
• Assessing the model performance using different performance metrics
• Underfitting and overfitting
• Exporting scikit-learn models to Core ML format
• Deploying trained models to iOS