Partial dependence plots visualize the sensitivity of a model to a specific parameter, while holding all other parameters constant. The goal is to identify which parameters have the greatest impact on the predicted outcome for each individual observation. To create partial dependence plots, you would typically: 1. Choose a specific parameter of interest and hold all other parameters constant. 2. Create a grid of values for the chosen parameter, spanning its plausible range (e.g., 0 to its maximum value). 3. Calculate the predicted outcomes for each combination of the held parameter and the remaining parameters. 4. Plot the predicted outcomes against the held parameter, with the other parameters held constant. This will generate a plot that shows how the predicted outcome changes as the specific parameter varies, while keeping all other parameters fixed. The resulting plot can help identify which parameters have the most significant impact on the model's predictions for individual data points. Alternative approaches include [[SHAP]] values or [[LIME]].