updated plot_posterior_predictive docstring

pymc_marketing/mmm/base.py

@@ -368,21 +368,51 @@ def plot_posterior_predictive(
         ax: plt.Axes = None,
         **plt_kwargs: Any,
     ) -> plt.Figure:
-        """Plot posterior distribution from the model fit.
+        """
+        Plot the posterior predictive distribution from the model fit.
+
+        This function creates a visualization of the model's posterior predictive distribution,
+        allowing for comparison with observed data. It can include highest density intervals (HDI),
+        mean predictions, and a gradient representation of the full distribution.
 
         Parameters
         ----------
         original_scale : bool, optional
-            Whether to plot in the original scale.
+            If True, plot in the original scale of the target variable.
+            If False, plot in the transformed scale used for modeling. Default is False.
+        add_hdi : bool, optional
+            If True, add highest density intervals to the plot. Default is True.
+        add_mean : bool, optional
+            If True, add the mean prediction to the plot. Default is True.
+        add_gradient : bool, optional
+            If True, add a gradient representation of the full posterior distribution. Default is False.
         ax : plt.Axes, optional
-            Matplotlib axis object.
-        **plt_kwargs
-            Keyword arguments passed to `plt.subplots`.
+            A matplotlib Axes object to plot on. If None, a new figure and axes will be created.
+        **plt_kwargs : dict
+            Additional keyword arguments to pass to plt.subplots() when creating a new figure.
 
         Returns
         -------
         plt.Figure
+            The matplotlib Figure object containing the plot.
 
+        Raises
+        ------
+        ValueError
+            If the length of the target variable doesn't match the length
+            of the date column in the posterior predictive data.
+
+        Notes
+        -----
+        This function visualizes the model's predictions against the observed data.
+        The observed data is always plotted as a black line.
+        Depending on the parameters, it can also show:
+        - HDI (Highest Density Intervals) at 94% and 50% levels
+        - Mean prediction line
+        - Gradient representation of the full posterior distribution
+
+        If predicting out-of-sample, ensure that `self.y` is overwritten with the
+        corresponding non-transformed target variable.
         """
         posterior_predictive_data: Dataset = self._get_posterior_predictive_data(
             original_scale=original_scale