```
library(mlr)
= list(
learners makeLearner("classif.svm", kernel = "linear"),
makeLearner("classif.svm", kernel = "polynomial"),
makeLearner("classif.svm", kernel = "radial"),
"classif.qda",
"classif.randomForest",
"classif.knn"
)
```

In this post I want to shortly introduce you to the great visualization possibilities of `mlr`

. Within the last months a lot of work has been put into that field. This post is not a tutorial but more a demonstration of how little code you have to write with `mlr`

to get some nice plots showing the prediction behaviors for different learners.

First we define a list containing all the learners we want to visualize. Notice that most of the `mlr`

methods are able to work with just the string (i.e. `"classif.svm"`

) to know what learner you mean. Nevertheless you can define the learner more precisely with `makeLearner()`

and set some parameters such as the `kernel`

in this example.

First we define the list of learners we want to visualize.

## Support Vector Machines

Now lets have a look at the different results and lets start with the SVM with a *linear kernel*.

`plotLearnerPrediction(learner = learners[[1]], task = iris.task)`

We can see clearly that in fact the decision boundary is indeed linear. Furthermore the misclassified items are highlighted and a 10-fold cross validation to obtain the mean missclassification error is executed.

For the *polynomial* and the *radial kernel* the decision boundaries already look a bit more sophisticated:

`plotLearnerPrediction(learner = learners[[2]], task = iris.task)`

`plotLearnerPrediction(learner = learners[[3]], task = iris.task)`

Note that the intensity of the colors also indicates the certainty of the prediction and that this example is probably a rare case where the linear kernel performs best. although this is likely only the case because we didn’t optimize the parameters for the radial kernel.

## Quadratic Discriminant Analysis

`plotLearnerPrediction(learner = learners[[4]], task = iris.task)`

A well known classificator from the basic course of statistics delivers a similar performance as the SVMs.

## Random Forest

`plotLearnerPrediction(learner = learners[[5]], task = iris.task)`

A completely different picture is generated by the random forest. Here you can see that the whole data set is used to generate the model and as a result it looks like it gives a perfect fit but obviously you wouldn’t use the train data to evaluate your model. And the results of the 10-fold cross validation indicate that the random forest is actually not better then the others.

## Nearest Neighbour

`plotLearnerPrediction(learner = learners[[6]], task = iris.task)`

In the default setting knn just look for ‘k=1’ neighbor and as a result the classifier does not return probabilities but only the class labels.