What does "Partial Dependence Plots" mean?

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Partial dependence plots are a way to show how a specific feature in a machine learning model affects the outcome while keeping other features constant. They help people see the relationship between one feature and the prediction, making it easier to understand what the model is doing.

When we look at partial dependence plots, we can see how changes in one feature influence the result. For example, if we want to know how age affects salary, a partial dependence plot can illustrate this by showing the expected salary for different ages, without the noise from other factors like experience or education.

These plots are helpful because they make complex models more transparent. By visualizing the effect of single features, users can gain insights into the decision-making process of the model, helping them trust and interpret its predictions better.