Machine Learning with Swift

Summary

In this chapter, we had our first experience of building a machine learning application, starting from the data and all the way over to the working iOS application. We went through several phases in this chapter:

• Exploratory data analysis using Jupyter, pandas, and Matplotlib
• Data preparation—splitting, and handling categorical variables
• Model prototyping using scikit-learn
• Model tuning and evaluation
• Porting prototype for the mobile platform using Core ML
• Model validation on a mobile device

There are several machine learning topics that we've learned about in this chapter: model parameters vs. hyperparameters, overfitting vs. underfitting, evaluation metrics: cross-validation, accuracy, precision, recall, and F1-score. These are ...