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Learning Jupyter

You're reading from   Learning Jupyter Select, Share, Interact and Integrate with Jupyter Notebook

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785884870
Length 238 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Jupyter 2. Jupyter Python Scripting FREE CHAPTER 3. Jupyter R Scripting 4. Jupyter Julia Scripting 5. Jupyter JavaScript Coding 6. Interactive Widgets 7. Sharing and Converting Jupyter Notebooks 8. Multiuser Jupyter Notebooks 9. Jupyter Scala 10. Jupyter and Big Data

Node.js decision-tree package


The decision-tree package is an example of a machine learning package. It is available at https://www.npmjs.com/package/decision-tree . The package is installed using the following command:

npm install decision-tree

We need a dataset to use for training/developing our decision tree. I am using the car MPG dataset on this page: https://alliance.seas.upenn.edu/~cis520/wiki/index.php?n=Lectures.DecisionTrees . It did not seem to be available directly, so I copied it into Excel and saved it as a local CSV.

The logic for machine learning is very similar:

  • Load our dataset

  • Split into a training set and a testing set

  • Use the training set to develop our model

  • Test the mode on the test set.

Tip

Typically, you might use two-thirds of your data for training and one-third for testing.

Using the decision-tree package and the car MPG dataset we would have a script similar to the following:

//Import the modules
var DecisionTree = require('decision-tree');
var fs = require("fs");...
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