Machine Learning for iOS Developers

Chapter 9Creating a Logistic Regression Model Using Scikit-learn and Core ML

WHAT'S IN THIS CHAPTER

• Training a logistic regression classifier with Scikit-learn
• Using Core ML Tools to convert a Scikit-learn model into the Core ML format
• Using the logistic regression model in an iOS application

In this chapter, you will use logistic regression to build a machine learning model that can be used to predict the onset of diabetes. The model will be trained on the Pima Indians diabetes dataset, a copy of which is included in the downloads that accompany this lesson. After the model is trained, you will use Core ML Tools to export the model into the Core ML file format and finally use the model in an iOS app that allows users to enter their information and determine whether they are likely to have type 2 diabetes.

NOTE  You can download the code files for this chapter from wrox.com or from GitHub using the following URL:

https://github.com/asmtechnology/iosmlbook-chapter9.git

Examining the Dataset

This chapter uses the Pima Indians diabetes dataset, which was originally created by the National Institute of Diabetes and Digestive and Kidney Diseases. The dataset itself is a sample of a much larger dataset and consists of data for women from the Pima Indian heritage. The dataset was originally hosted at the UCI ML repository at http://www.ics.uci.edu/~mlearn/MLRepository.html. The dataset has been used in several studies and research papers. A copy of the dataset is also ...