Exercise3: My first model

• Install the caret package
• Install the ranger package
• Install the tree package

Decision trees

• Split your data into a training and testing set with caret::createDataPartition(). The training set should inherit 60% of the data. The testing set the other 40% accoringly.
• Train a decision tree with tree::tree() using your training set.
• Plot the decision tree. You have to use both plot(model) and text(model) to get a plot.

Random forest

Now we train many more trees: a random forest.

• Train a random forest model with ranger::ranger() using your training set.
• Predict the mean vegetation height on your testing set.
• Create a scatterplot with the actual mean vegetation height on the x-axis and the predicted mean vegetation height on the y-axis.