# Applying machine learning algorithms – exercises

**R-exercises**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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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.

**Exercise 1**

Create a list named “control” that runs a 10-fold cross-validation. **HINT**: Use `trainControl()`

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**Exercise 2**

Use the metric of “Accuracy” to evaluate models.

**Exercise 3**

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

**Exercise 4**

Create a list of the 5 models you just built and name it “results”. **HINT**: Use `resamples()`

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**Exercise 5**

Report the accuracy of each model by using the summary function on the list “results”. **HINT**: Use `summary()`

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**Exercise 6**

Create a plot of the model evaluation results and compare the spread and the mean accuracy of each model. **HINT**: Use `dotplot()`

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**Exercise 7**

Which model seems to be the most accurate?

**Exercise 8**

Summarize the results of the best model and print them. **HINT**: Use `print()`

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**Exercise 9**

Run the “LDA” model directly on the validation set to create a factor named “predictions”. **HINT**: Use `predict()`

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**Exercise 10**

Summarize the results in a confusion matrix. **HINT**: Use `confusionMatrix()`

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