Decision tree visualisation

For the paper, it would be really useful to visualise the output tree. Apparently this ias actually really easy with scikit-learn:

```
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from matplotlib import pyplot as plt

dataset=np.concatenate([mat_original,np.expand_dims(y_km,axis=1)],axis=1)
dt = DecisionTreeClassifier(max_depth=15, min_samples_leaf=30)
classifier=dt.fit(dataset[:,:-1], dataset[:,-1])
fig, axes=plt.subplots(1, 1, figsize=(400, 200), sharex=True, sharey=True)
tree.plot_tree(classifier)
fig.savefig('tree.png')
```

It would be interesting to combine this code with the idea to make visualisation in a notebook easy. We could create a wrapper of the class that has a `_to_html()` method so that when you execute a cell with just the tree object name, it prints out a graph, like the nice options you get with iris cubes or pandas dataframes in a jupyter notebook. This could potentially be fed back to scikit learn in some way.