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An Intro to Regression Analysis With Decision Trees

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An Intro to Regression Analysis With Decision Trees

A collection of interesting snippets for scientific computing and data visualization in Python.

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It's a while that there are no posts on this blog, but the Glowing Python is still active and strong! I just decided to publish some of my post on the Cambridge Coding Academy blog. Here are the links to a series of two posts about Regression Analysis with Decision Trees:

  1. Getting started with Regression Analysis and Decision Trees
  2. From simple Regression to Multiple Regression with Decision Trees

In this introduction to Regression Analysis we will see how to user scikit-learn to train Decision Trees to solve a specific problem: "How can we predict the number of bikes hired in a bike sharing system in a given day?"

In the first post, we will see how to train a simple Decision Tree to exploit the relation between temperature and bikes hired, this tree will be analyzed to explain the result of the training process and gain insights about the data. In the second, we will see how to learn more complex decision trees and how to assess the accuracy of the prediction using cross validation.

Here's a sneak peak of the figures that we will generate:

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Topics:
scikit-learn ,prediction ,regression ,machine learning ,decision trees

Published at DZone with permission of Giuseppe Vettigli, DZone MVB. See the original article here.

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