plot_interactions ================= .. py:module:: plot_interactions .. autoapi-nested-parse:: Interaction effect plotting module for ML results analysis. Focuses on visualizing how pairs of parameters jointly affect model performance. Classes ------- .. autoapisummary:: plot_interactions.InteractionPlotter Module Contents --------------- .. py:class:: InteractionPlotter(data: pandas.DataFrame) Initializes the InteractionPlotter. :param data: Results DataFrame, must contain columns for parameters and performance metrics. :type data: pd.DataFrame .. py:attribute:: data .. py:attribute:: clean_data .. py:method:: plot_categorical_interaction(param1: str, param2: str, metric: str = 'auc', figsize: Tuple[int, int] = (10, 8)) -> None Visualizes the interaction of two categorical parameters via a heatmap. :param param1: The name of the first categorical parameter column. :type param1: str :param param2: The name of the second categorical parameter column. :type param2: str :param metric: The performance metric to plot. Defaults to 'auc'. :type metric: str, optional :param figsize: Figure size for the plot. Defaults to (10, 8). :type figsize: Tuple[int, int], optional .. py:method:: plot_continuous_interaction(param1: str, param2: str, metric: str = 'auc', figsize: Tuple[int, int] = (10, 8)) -> None Visualizes the interaction of two continuous parameters. Uses a scatter plot where point color represents the metric value. :param param1: The name of the first continuous parameter column. :type param1: str :param param2: The name of the second continuous parameter column. :type param2: str :param metric: The performance metric to use for color. Defaults to 'auc'. :type metric: str, optional :param figsize: Figure size for the plot. Defaults to (10, 8). :type figsize: Tuple[int, int], optional