Partial Dependence & ICE Plot Explorer (2D): Understanding Model Behavior

What Partial Dependence & ICE Plots Are

Partial dependence plots (PDP) and individual conditional expectation (ICE) curves are graphical tools used in machine learning to visualize the relationship between a model's predictions and one or more features. PDP averages the model’s response over many instances for specific values of a feature, while ICE curves show how each instance’s prediction varies with that same feature.

These plots help us understand how changes in input features affect model outputs, making them invaluable for interpreting complex models like those used in predictive analytics and data science.

Why It Matters

Understanding the behavior of a model through PDP and ICE plots is crucial for ensuring that the model’s predictions are reliable and interpretable. By visualizing these relationships, we can identify potential biases or interactions between features, which might not be apparent from looking at raw data alone.

Moreover, these tools aid in explaining model decisions to stakeholders who may lack technical expertise, thereby enhancing trust and facilitating informed decision-making.

How PDP & ICE Plots Work Together

When you interact with the Partial Dependence & ICE Plot Explorer (2D), you can observe how sweeping one feature while keeping another constant influences model predictions. The PDP shows an average of these individual responses, providing a general trend across the feature space. Meanwhile, ICE curves display the exact response for each instance, revealing variations and outliers.

The interaction strength slider allows you to see how different levels of interaction between features affect the PDP and ICE plots, illustrating complex relationships that might not be captured by simple linear models.

Real-World Applications

In finance, PDP and ICE plots can help analysts understand how changes in interest rates or credit scores affect loan approval probabilities. In healthcare, these tools can reveal the impact of different patient characteristics on treatment outcomes.

By providing clear visualizations of model behavior, these techniques enable data scientists to build more accurate and transparent models, ultimately leading to better-informed decisions across various industries.

Frequently asked questions

Try it live

Everything above runs in your browser — open Partial Dependence & ICE Plot Explorer (2D) and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.