Since Khiops does not have hyperparameters, how can I manage overfitting?

[lucaurelien]

This discussion is based on a question received via our contact form:

“Khiops does not have hyperparameters to tune, so how does it avoid overfitting? Are there specific mechanisms or best practices to ensure generalization when working with noisy datasets?”

[lucaurelien]

Khiops avoids overfitting through its core design, which is based on the MDL (Minimum Description Length) principle. This formalism balances the complexity of a model with its ability to explain the data, making Khiops particularly robust.

Here’s why Khiops excels at avoiding overfitting:

• Statistically significant patterns only: Khiops selects only patterns supported by sufficient data, automatically rejecting noise.
• No arbitrary parameters: By avoiding user-defined hyperparameters, Khiops streamlines workflows and reduces the risk of overfitting caused by over-tuning.

How Khiops achieves this:

Unlike standard models that rely on regularization parameters to control the trade-off between complexity and generalization, Khiops achieves this balance intrinsically through a mechanism rooted in information theory. Our original formalism penalizes unnecessary complexity by favoring models that explain the data as simply as possible.
This ensures that every variable, interval, or aggregate is justified by the amount of information it provides, without requiring external tuning. For instance:

• A complex derived feature from multi-table data is included only if it significantly improves the model.
• Similarly, for the encoding of a variable, an interval or a group will only be added if it provides sufficient information to justify its inclusion.

Khiops naturally handles noisy datasets by ignoring irrelevant patterns:

• When enough meaningful data is present, noise doesn’t affect the model (even if highly present).
• If the dataset contains too much noise and insufficient data, Khiops will prudently return one single interval.

While this cautious behavior is a key advantage, it also means that Khiops performs better and better with more data (building powerful models requires enough data to justify more complex constructs).

Illustration

The graph below shows how Khiops’ MODL approach handles the discretization of the “crenel pattern” Class = Sign(Sinus(100πx)), with 10% misclassified instances (as described in Boulle, 2006, Figure 18). The x-axis represents the number of instances available in the dataset, while the y-axis shows the number of intervals created by the discretization process. This example is particularly illustrative because it demonstrates how MODL balances complexity and informativity, even in the presence of noise, while avoiding overfitting.

• Insufficient or noisy data: When there are too few instances or excessive noise, Khiops keeps only one interval, avoiding unnecessary complexity (such numerical variables will be ignored in the final model as they are considered uninformative).
• Optimal intervals: As more data becomes available, Khiops adjusts dynamically, creating an optimal number of intervals to reflect the data’s structure.
• No overfitting: The number of intervals does not grow indefinitely. In this example, Khiops concludes that 100 intervals are sufficient. Adding more data does not produce spurious intervals, which prevents overfitting