``treemind.plot`` ================= .. autofunction:: treemind.plot.feature_plot **Example Usage** Below is an example of how to use the `feature_plot` function: .. code-block:: python from treemind import Explainer from treemind.plot import feature_plot # Assume 'model' is a trained lightgbm or xgboost or catboost model object # Create an instance of the Explainer explainer = Explainer() explainer(model) # Analyze the specified feature by its index df = tree.analyze_feature(22) # Plot the feature using a line plot feature_plot(df) **Output** .. image:: _static/api/feature_plot.png :alt: feature_plot example .. autofunction:: treemind.plot.interaction_plot **Example Usage** Below is an example of how to use the `interaction_plot` function: .. code-block:: python from treemind import Explainer from treemind.plot import interaction_plot # Assume 'model' is a trained lightgbm or xgboost or catboost model object # Create an instance of the Explainer explainer = Explainer() explainer(model) # Analyze the specified feature by its index df = tree.analyze_feature([22,21]) # Plot the feature using a line plot interaction_plot(df) **Output** .. image:: _static/api/interaction_plot.png :alt: interaction_plot example .. autofunction:: treemind.plot.interaction_scatter_plot **Example Usage** Below is an example of how to use the `interaction_scatter_plot` function: .. code-block:: python from treemind import Explainer from treemind.plot import interaction_scatter_plot # Assume 'model' is a trained lightgbm or xgboost or catboost model object # Create an instance of the Explainer explainer = Explainer() explainer(model) # Analyze the specified feature by its index df = tree.analyze_feature([22,21]) # Plot the feature using a line plot interaction_scatter_plot(X, df, 22, 21) **Output** .. image:: _static/api/interaction_scatter_plot.png :alt: interaction_scatter_plot example