<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/basic-tree-2-exercises/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://r-exercises.com/2016/12/15/basic-tree-2/"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a target="_blank" rel="nofollow" href="http://r-exercises.com/wp-content/uploads/2016/11/treeplanting.png?x61433"><img sizes="(max-width: 150px) 100vw, 150px" alt="treeplanting" class="wp-image-3097" src="https://i2.wp.com/r-exercises.com/wp-content/uploads/2016/11/treeplanting-150x150.png?resize=150%2C150" data-recalc-dims="1"></a></p>
<p>This is a continuation of the exercise <a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/12/09/basic-tree-1/">Basic Tree 1</a></p>
<p>Answers to the exercises are available <a target="_blank" rel="nofollow" href="http://r-exercises.com/2016/12/15/basic-tree-2-2/">here</a>.</p>
<p>If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.</p>
<p><strong>Exercise 1</strong><br>
load the tree library. If it is not installed then use the install.packages() command to install it.</p>
<p><strong>Exercise 2</strong><br>
Convert all the feaures(columns) into factors, including the class column</p>
<p><strong>Exercise 3</strong><br>
Use the sample methods that you learnt from the sample_exercise to split the data into two sets with a SplitRatio of 0.7. Hint: Use caTools library and sample.split() function. Store the results into Train and Test.</p>
<p><strong>Exercise 4</strong><br>
Use the <code>tree()</code> command to build the model. Use class as the target variable and everything else as the predictor variable. Also, use the Train variable as the data source. Store the model in a variable called model1</p>
<p><strong>Exercise 5</strong><br>
Use the <code>plot()</code> command to plot the model and use the text() command to add the text.</p>
<p><strong>Exercise 6</strong></p>
<p>Use the <code>predict()</code> command to predict the classes using the Test dataset. We want to predict the classes. Store this in the variable pred_class</p>
<p><strong>Exercise 7</strong></p>
<p>Use the <code>table()</code> command to print the confusion matrix. Hint: You are comparing the class from the Test set and the predicted vector. This tells you wether the model is answering anything right or wrong</p>
<p><strong>Exercise 8</strong><br>
use the <code>summary()</code> to print the summary of the model and note the misclassification error rate.</p>
<p><strong>Exercise 9</strong><br>
Now find the misclassification error rate of the model on the Test data. Use the formula. mean(Test$class != pred_class)</p>
<p><strong>Exercise 10</strong><br>
Compare the two misclassification error rates and determine which is worse and why. How can we improve the model?</p>
<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://r-exercises.com/2016/12/15/basic-tree-2/"> R-exercises</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a title="The R Project for Statistical Computing" rel="nofollow" href="https://www.r-project.org/">R</a> news and <a title="R tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/tutorial">tutorials</a> on topics such as: <a title="Data science" rel="nofollow" href="https://www.r-bloggers.com/search/data%20science">Data science</a>, <a title="Big Data" rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data">Big Data, <a title="R jobs" rel="nofollow" href="https://www.r-users.com/">R jobs</a>, visualization (<a title="ggplot and ggplot2 tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2">ggplot2</a>, <a title="Boxplots using lattice and ggplot2 tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/boxplot">Boxplots</a>, <a title="Maps and gis" rel="nofollow" href="https://www.r-bloggers.com/search/map">maps</a>, <a title="Animation in R" rel="nofollow" href="https://www.r-bloggers.com/search/animation">animation</a>), programming (<a title="RStudio IDE for R" rel="nofollow" href="https://www.r-bloggers.com/search/RStudio">RStudio</a>, <a title="Sweave and literate programming" rel="nofollow" href="https://www.r-bloggers.com/search/sweave">Sweave</a>, <a title="LaTeX in R" rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX">LaTeX</a>, <a title="SQL and databases" rel="nofollow" href="https://www.r-bloggers.com/search/SQL">SQL</a>, <a title="Eclipse IDE for R" rel="nofollow" href="https://www.r-bloggers.com/search/eclipse">Eclipse</a>, <a title="git and github, Version Control System" rel="nofollow" href="https://www.r-bloggers.com/search/git">git</a>, <a title="Large data in R using Hadoop" rel="nofollow" href="https://www.r-bloggers.com/search/hadoop">hadoop</a>, <a title="Web Scraping of google, facebook, yahoo, twitter and more using R" rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping">Web Scraping</a>) statistics (<a title="Regressions and ANOVA analysis tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/regression">regression</a>, <a title="principal component analysis tutorial" rel="nofollow" href="https://www.r-bloggers.com/search/PCA">PCA</a>, <a title="Time series" rel="nofollow" href="https://www.r-bloggers.com/search/time+series">time series</a>, <a title="finance trading" rel="nofollow" href="https://www.r-bloggers.com/search/trading">trading</a>) and more...
<img height="1" alt="" width="1" src="http://feeds.feedburner.com/~r/RBloggers/~4/OiadP7_R_vM"></a></div>