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Applying machine learning algorithms – exercises

Applying machine learning algorithms – exercises -  #machinelearning #IoT #AI #BigData

  • If you are a newbie in the world of machine learning, then this tutorial is exactly what you need in order to introduce yourself to this exciting new part of the data science world.
  • This post includes a full machine learning project that will guide you step by step to create a “template,” which you can use later on other datasets.
  • Learn more about machine learning in the online course Quickly dive into more advanced methods in an accessible pace and with more explanations And much more about machine learning in the online course Beginner to Advanced Guide on Machine Learning with R Tool .
  • Report the accuracy of each model by using the summary function on the list “results”.
  • Create a plot of the model evaluation results and compare the spread and the mean accuracy of each model.

(This article was first published on R-exercises, and kindly contributed to R-bloggers) INTRODUCTION Dear reader, If you are a newbie in the world of

@abunchofdata: Applying machine learning algorithms – exercises – #machinelearning #IoT #AI #BigData

Dear reader,

If you are a newbie in the world of machine learning, then this tutorial is exactly what you need in order to introduce yourself to this exciting new part of the data science world.

This post includes a full machine learning project that will guide you step by step to create a “template,” which you can use later on other datasets.

Before proceeding, please follow our short tutorial.

Look at the examples given and try to understand the logic behind them. Then try to solve the exercises below using R and without looking at the answers. Then see the solutions to check your answers.

Use the metric of “Accuracy” to evaluate models.

Exercise 3

Build the “LDA”, “CART”, “kNN”, “SVM” and “RF” models.

Which model seems to be the most accurate?

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(as of September 15, 2017 11:53 pm – More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)

(as of September 15, 2017 11:53 pm – More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)

(as of September 15, 2017 11:53 pm – More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)

(as of September 15, 2017 11:53 pm – More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)

(as of September 15, 2017 11:53 pm – More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)

(as of September 15, 2017 11:53 pm – More infoProduct prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on [relevant Amazon Site(s), as applicable] at the time of purchase will apply to the purchase of this product.)

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Applying machine learning algorithms – exercises

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