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Clicking the “Update” button runs and refreshes the R code and results without refreshing the notebook. These are just for starters, check out the rest of them Why RStudio? Kaggle Notebooks has a neat feature called "Learn to Code". This allows you to run code, right from the Kaggle Notebook, without having to load a new Notebook. You can find this feature in the left hand navigation in the "R" pane. It's a great way to see if your code works, or even to learn R. I suggest you try it, you might even have fun! All of the codes in this Notebook are run using the Python language in Kaggle Notebooks, but if you're interested in running codes using the R language, you can use the below code. This will run an entire R script (with all the R functions you have defined) and display the result in the right-hand pane. 3. Open "mydata" in RStudio and analyze the data I've uploaded a sample of my data in a file called "mydata.txt" to Github. Read it in RStudio using the code below: mydata.txt In this file, you can see that there are two different columns named "agent" and "principal", where agent = "Principal" in some columns, and agent = "Agent" in other columns. This indicates that our labels have been reversed. The agent and principal columns are used to separate and label each of the data points into either "agent" or "principal" category. In our case, we have a total of seven agents and five principals. There are seven agents because 787 (the highest count in the "agent" column) and there are five principals because five agents have been labeled "principal" in the other columns. I ran the code below to create the principal_agent vector, with the true values in bold, which means these are the principal values. I also created a confusion matrix to show the performance of the classifier. principal_agent
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