Machine Learning with Swift

Tree visualization

Let us take a look at the code to visualize a tree as follows:

In []: 
labels = df.label.astype('category').cat.categories 
labels = list(labels) 
labels 
Out[]: 
[u'platyhog', u'rabbosaurus']  

Define a variable to store all the names for the features:

In []: 
feature_names = map(lambda x: x.encode('utf-8'), features.columns.get_values()) 
feature_names 
Out[]: 
['length', 
 'fluffy', 
 'color_light black', 
 'color_pink gold', 
 'color_purple polka-dot', 
 'color_space gray'] 

Then, create the graph object using the export_graphviz function:

In []: import pydotplus dot_data = tree.export_graphviz(tree_model, out_file=None, feature_names=feature_names, class_names=labels, filled=True, rounded=True, special_characters=True) dot_data Out[]: ...