dtreeviz

Quick start

See Installation instructions then take a look at the specific notebooks for the supported ML library you're using:

• sklearn-based examples (colab)
• LightGBM-based examples (colab)
• Spark-based examples (colab)
• TensorFlow-based examples (colab) Also see blog at tensorflow.org Visualizing TensorFlow Decision Forest Trees with dtreeviz
• XGBoost-based examples (colab)
• Classifier decision boundaries for any scikit-learn model.ipynb (colab)
• Changing colors notebook (colab)
• AI-powered tree analysis (sklearn) - Interactive chat and explanations using LLMs

To interopt with these different libraries, dtreeviz uses an adaptor object, obtained from function dtreeviz.model(), to extract model information necessary for visualization. Given such an adaptor object, all of the dtreeviz functionality is available to you using the same programmer interface. The basic dtreeviz usage recipe is:

1. Import dtreeviz and your decision tree library
2. Acquire and load data into memory
3. Train a classifier or regressor model using your decision tree library
4. Obtain a dtreeviz adaptor model using
   viz_model = dtreeviz.model(your_trained_model,...)
5. Call dtreeviz functions, such as
   viz_model.view() or viz_model.explain_prediction_path(sample_x)

Example

Here's a complete example Python file that displays the following tree in a popup window:

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

import dtreeviz

iris = load_iris()
X = iris.data
y = iris.target

clf = DecisionTreeClassifier(max_depth=4)
clf.fit(X, y)

viz_model = dtreeviz.model(clf,
                           X_train=X, y_train=y,
                           feature_names=iris.feature_names,
                           target_name='iris',
                           class_names=iris.target_names)

v = viz_model.view()     # render as SVG into internal object 
v.show()                 # pop up window
v.save("/tmp/iris.svg")  # optionally save as svg

In a notebook, you can render inline without calling show(). Just call view():

viz_model.view()       # in notebook, displays inline

---

Installation

Install anaconda3 on your system, if not already done.

You might verify that you do not have conda-installed graphviz-related packages installed because dtreeviz needs the pip versions; you can remove them from conda space by doing:

conda uninstall python-graphviz
conda uninstall graphviz

To install (Python >=3.6 only), do this (from