|--- feature_2 <= 2.45
| |--- class: 0
|--- feature_2 > 2.45
| |--- feature_3 <= 1.75
| | |--- feature_2 <= 4.95
| | | |--- feature_3 <= 1.65
| | | | |--- class: 1
| | | |--- feature_3 > 1.65
| | | | |--- class: 2
| | |--- feature_2 > 4.95
| | | |--- feature_3 <= 1.55
| | | | |--- class: 2
| | | |--- feature_3 > 1.55
| | | | |--- feature_0 <= 6.95
| | | | | |--- class: 1
| | | | |--- feature_0 > 6.95
| | | | | |--- class: 2
| |--- feature_3 > 1.75
| | |--- feature_2 <= 4.85
| | | |--- feature_1 <= 3.10
| | | | |--- class: 2
| | | |--- feature_1 > 3.10
| | | | |--- class: 1
| | |--- feature_2 > 4.85
| | | |--- class: 2
If you want to save it to the file, it can be done with following code:
with open("decistion_tree.log", "w") as fout:
fout.write(text_representation)
Plot Tree with plot_tree
The plot_tree method was added to sklearn in version 0.21. It requires matplotlib to be installed. It allows us to easily produce figure of the tree (without intermediate exporting to graphviz) The more information about plot_tree arguments are in the docs.
fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(clf,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True)
(The plot_tree returns annotations for the plot, to not show them in the notebook I assigned returned value to _.)
To save the figure to the .png file:
fig.savefig("decistion_tree.png")
Please notice that I’m using filled=True in the plot_tree. When this parameter is set to True the method uses color to indicate the majority of the class. (It will be nice if there will be some legend with class and color matching.)
Visualize Decision Tree with graphviz
Please make sure that you have graphviz installed (pip install graphviz). To plot the tree first we need to export it to DOT format with export_graphviz method (link to docs).
Then we can plot it in the notebook or save to the file.
import graphviz
# DOT data
dot_data = tree.export_graphviz(clf, out_file=None,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True)
# Draw graph
graph = graphviz.Source(dot_data, format="png")
graph
Plot Decision Tree with dtreeviz Package
The dtreeviz package is available in github. It can be installed with pip install dtreeviz. It requires graphviz to be installed (but you dont need to manually convert between DOT files and images). To plot the tree just run:
from dtreeviz.trees import dtreeviz # remember to load the package
viz = dtreeviz(clf, X, y,
target_name="target",
feature_names=iris.feature_names,
class_names=list(iris.target_names))
Visualizing the Decision Tree in Regression Task
Below, I present all 4 methods for DecisionTreeRegressor from scikit-learn package (in python of course).
from sklearn import datasets
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
# Prepare the data data
boston = datasets.load_boston()
X = boston.data
y = boston.target
To keep the size of the tree small, I set max_depth = 3.
# Fit the regressor, set max_depth = 3
regr = DecisionTreeRegressor(max_depth=3, random_state=1234)
model = regr.fit(X, y)
text_representation = tree.export_text(regr)
print(text_representation)
|--- feature_5 <= 6.94
| |--- feature_12 <= 14.40
| | |--- feature_7 <= 1.38
| | | |--- value: [45.58]
| | |--- feature_7 > 1.38
| | | |--- value: [22.91]
| |--- feature_12 > 14.40
| | |--- feature_0 <= 6.99
| | | |--- value: [17.14]
| | |--- feature_0 > 6.99
| | | |--- value: [11.98]
|--- feature_5 > 6.94
| |--- feature_5 <= 7.44
| | |--- feature_4 <= 0.66
| | | |--- value: [33.35]
| | |--- feature_4 > 0.66
| | | |--- value: [14.40]
| |--- feature_5 > 7.44
| | |--- feature_10 <= 19.65
| | | |--- value: [45.90]
| | |--- feature_10 > 19.65
| | | |--- value: [21.90]
fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(regr, feature_names=boston.feature_names, filled=True)
Please notice, that the color of the leaf is coresponding to the predicted value.
dot_data = tree.export_graphviz(regr, out_file=None,
feature_names=boston.feature_names,
filled=True)
graphviz.Source(dot_data, format="png")
from dtreeviz.trees import dtreeviz # remember to load the package
viz = dtreeviz(regr, X, y,
target_name="target",
feature_names=boston.feature_names)
From above methods my favourite is visualizing with dtreeviz package. I like it becuause:
it shows the distribution of decision feature in the each node (nice!)
it shows the class-color matching legend
it shows the distribution of the class in the leaf in case of classification tasks, and mean of the leaf’s reponse in the case of regression tasks
It would be great to have dtreeviz visualization in the interactive mode, so the user can dynamically change the depth of the tree. I’m using dtreeviz package in my Automated Machine Learning (autoML) Python package mljar-supervised. You can check the details of the implementation in the github repository. One important thing is, that in my AutoML package I’m not using decision trees with max_depth greater than 4. I add this limit to not have too large trees, which in my opinion loose the ability of clear understanding what’s going on in the model. Below is the example of the markdown report for Decision Tree generated by mljar-supervised.
A Decision Tree is a supervised algorithm used in machine learning. It is using a binary tree graph (each node has two children) to assign for each data sample a target value. The target values are presented in the tree leaves. To reach to the leaf, the sample is propagated through nodes, starting at the root node. In each node a decision is made, to which descendant node it should go. A decision is made based on the selected sample’s feature. Decision Tree learning is a process of finding the optimal rules in each internal tree node according to the selected metric.