dtreeviz
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parrt
A python library for decision tree visualization and model interpretation.
@parrt it contains the visualisations for Random Forest algo. The tensorflow DF contains more algorithms for decision forests and we can add them also in the future. close #181
https://blog.tensorflow.org/2021/05/introducing-tensorflow-decision-forests.html
Have an issue with an import ```bash % python3.8 -m pip install colour Requirement already satisfied: colour in /Users/***/miniconda3/lib/python3.8/site-packages (0.1.5) % python3.8 Python 3.8.11 (default, Jul 29 2021, 14:57:32) [Clang...
Nodes can contain more informations like impurity and number of samples which we can include in visualisations. A feature request from this article : https://towardsdatascience.com/everything-you-ever-wanted-to-know-about-decision-trees-in-python-17e8edb6b37b
Apparently `dtreeviz` in `dtreeviz.trees.py` fails on the following block of code: ```python > /home/macermak/.local/lib/python3.6/site-packages/dtreeviz/trees.py(320)dtreeviz() 318 319 n_classes = shadow_tree.nclasses() --> 320 color_values = color_blind_friendly_colors[n_classes] 321 322 # Fix the mapping...
Discover which are the most consuming parts for each visualisations/libraries and try to optimise them.
Hi dtreeviz, I'm trying to visualise a decision tree of an XGBoost regressor model using ShadowXGBDTree(). I'm loading a pre-trained XGBoost model. I can visualise using graphviz and pydotplus, and...
```python import xgboost as xgb from sklearn.datasets import load_diabetes from sklearn.model_selection import train_test_split diabetes = load_diabetes() feature_names = diabetes.feature_names X = diabetes.data Y = diabetes.target test_ratio = 0.2 x_train, x_test,...
If I have a multi output decision tree, such as https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression_multioutput.html is this intended to be supported? I did note that target_name can be specified, as seen in your example...
UPDATE: found problem and fix. to fix change line 319 in classifiers.py: `color_map = {v: class_colors[i] for i, v in enumerate(class_values)} ## changed from color_map = {i: class_colors[i] for i,...
