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.

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 section below.

Getting Started

To get started, follow the directions on the Setup section below to download data and install a Python interpreter.

Setup

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-intro on 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 data directory by clicking on the directory names leading to it: Desktop, swc-python, then data:
Launch the notebook by clicking on the “New” button and then selecting “Python 3”:

Here the setup depends if you are using a Unix shell (Terminal or Console in macOS and Linux, or Git Bash on Windows) or the Windows Command Prompt.

Unix shell

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:

BASH

cd ~/Desktop/swc-python/data

Then, execute this command to start the Jupyter server:

BASH

jupyter notebook

Command Prompt (Windows)

Navigate to the data directory: 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\data

Then, execute this command to start the Jupyter server:

python -m notebook

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.