Summary and Setup
This lesson provides an introduction to some of the common methods and terminologies used in machine learning research. We cover areas such as data preparation and resampling, model building, and model evaluation.
It is a prerequisite for the other lessons in the machine learning curriculum. In later lessons we explore tree-based models for prediction, neural networks for image classification, and responsible machine learning.
Predicting the outcome of critical care patients
Critical care units are home to sophisticated monitoring systems, helping carers to support the lives of the sickest patients within a hospital. These monitoring systems produce large volumes of data that could be used to improve patient care.
Our goal will be to predict the outcome of critical care patients using physiological data available on the first day of admission to the intensive care unit. These predictions could be used for resource planning or to assist with family discussions.
The dataset used in this lesson was extracted from the eICU Collaborative Research Database, a publicly available dataset comprising deidentified physiological data collected from critically ill patients.
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Prerequisites
You need to understand the basics of Python before tackling this lesson. The lesson sometimes references Jupyter Notebook although you can use any Python interpreter mentioned in the [Setup][lesson-setup].
Overview
This lesson is designed to be run on a personal computer. All of the software and data used in this lesson are freely available online, and instructions on how to obtain them are provided below.
Install Python
In this lesson, we will be using Python 3 with some of its most popular scientific libraries. Although one can install a plain-vanilla Python and all required libraries by hand, we recommend installing Anaconda, a Python distribution that comes with everything we need for the lesson. Detailed installation instructions for various operating systems can be found on The Carpentries template website for workshops and in Anaconda documentation.
Obtain lesson materials
- Download eicu_cohort.csv.
- Create a folder called
carpentries-ml-introon your Desktop. - Move downloaded files to
carpentries-ml-intro.
Launch Python interface
To start working with Python, we need to launch a program that will interpret and execute our Python commands. Below we list several options. If you don’t have a preference, proceed with the top option in the list that is available on your machine. Otherwise, you may use any interface you like.
Option A: Jupyter Notebook
A Jupyter Notebook provides a browser-based interface for working with Python. If you installed Anaconda, you can launch a notebook in two ways:
- Launch Anaconda Navigator. It might ask you if you’d like to send
anonymized usage information to Anaconda developers:
Make your choice and click “Ok,
and don’t show again” button. - Find the “Notebook” tab and click on the “Launch” button:
Anaconda will open a new
browser window or tab with a Notebook Dashboard showing you the contents
of your Home (or User) folder. - Navigate to the
datadirectory by clicking on the directory names leading to it:Desktop,swc-python, thendata:
- Launch the notebook by clicking on the “New” button and then
selecting “Python 3”:
1. Navigate to the data directory:
If you’re using a Unix shell application, such as Terminal app in macOS, Console or Terminal in Linux, or Git Bash on Windows, execute the following command:
On Windows, you can use its native Command Prompt program. The
easiest way to start it up is pressing Windows Logo
Key+R, entering cmd, and hitting
Return. In the Command Prompt, use the following command to
navigate to the data folder:
cd /D %userprofile%\Desktop\swc-python\data2. Start Jupyter server
python -m notebook3. Launch the notebook by clicking on the “New” button on the right
and selecting “Python 3” from the drop-down menu: 
Option B: Cloud Notebook
Colaboratory, or “Colab”, is a cloud service that allows you to run a Jupyter-like Notebook in a web browser. To open a notebook, visit the Colaboratory website. You can upload your datasets using the “Files” panel on the left side of the page.
