In the field of machine learning, the ability to interpret what a model has learned has become a critical differentiator, especially when deploying solutions in productive environments where decisions affect people, processes, or investments. However, most existing interpretability methods focus on explaining after training, leaving aside the opportunity to guide one's learning towards behaviors aligned with expert knowledge. A key question arises here: what if we could incorporate prior knowledge naturally during training, without the need to label specific regions or characteristics?
This paper explores a novel strategy that uses partial dependence as a guiding mechanism for neural networks. Partial dependence, a classic tool in statistics and data analysis, allows us to visualize how the average prediction of a model varies when modifying the value of one or two characteristics, keeping the others constant. What's innovative is to employ that same function as part of the loss function during training, so that the neural network learns to respond according to an expected, user-defined functional form. Instead of forcing attention on specific input regions, the response of the model is conditioned in the space of the features, which is especially useful in regression problems and modeling of dynamical systems.
Imagine, for example, a model that must predict the evolution of a physical variable such as temperature in a chemical reactor. We know that temperature responds monotonously to pressure and with a delay with respect to the opening of a valve. With the traditional approach, we would have to manually tag thousands of examples for the network to learn those relationships. Instead, using guided partial dependence, we can express that knowledge as an expected curve and adjust the model so that its average response matches it. This not only speeds up training, but improves data efficiency, allowing comparable performance to be achieved with much smaller training sets.
Experiments carried out in areas such as time series forecasting and chaotic system modelling show that models trained with this technique not only fit the data better, but that their interpretations are consistent with expert knowledge. This contrasts with unguided models, which often learn spurious correlations or unnatural responses that, although they minimize error in training, fail in generalization and in confidence on the part of users. In sectors such as industry, energy or finance, where transparency is a regulatory or business requirement, this ability to align the model with prior knowledge becomes invaluable.
Behind this approach is a broader reflection: artificial intelligence should not be a black box that simply optimizes a statistical metric. Companies adopting AI for business demand solutions that not only predict well, but do so for the right reasons. This is where the combination of explainability techniques and guided training allows us to build more robust, auditable models aligned with business logic.
For organizations that want to integrate these types of innovations into their processes, having a technology partner that understands both theory and practice is critical. At Q2BSTUDIO, we develop custom applications that incorporate advanced artificial intelligence, from the design phase to deployment in production environments. Our team approaches each project with a holistic view: we not only implement algorithms, but we design the data architecture, validation strategies, and interfaces that allow business teams to interact with the models naturally.
In addition, infrastructure plays a decisive role. A trained model with guided partial dependency may require parallel runs or storage of large volumes of simulations. That's why we offer AWS and Azure cloud services that ensure scalability, security, and availability. We combine these cloud capabilities with business intelligence tools such as power bi to visualize the behavior of models and learned dependencies, facilitating data-driven decision-making.
Another relevant aspect is cybersecurity. When a model is trained on sensitive data or deployed in critical environments, information protection and system integrity are a priority. That's why we at Q2BSTUDIO also integrate cybersecurity into every phase of software development, ensuring that AI solutions are as reliable as they are accurate.
The concept of AI agents, understood as autonomous systems capable of perceiving their environment and acting to achieve objectives, benefits directly from this technique. An agent who must navigate a physical or virtual environment can be trained so that its perceptions (e.g., distance to an obstacle) have a partial dependence on the action it produces, following a behavior predefined by a human designer. This accelerates reinforcement learning and reduces the need for massive simulations.
On the other hand, process automation is enhanced when AI models can be fine-tuned with expert knowledge. For example, on a production line, a predictive control system that adjusts parameters in real time can be guided with partial dependence to respect physical limits or operator preferences. At Q2BSTUDIO we offer process automation using software that integrates these approaches, striking a balance between efficiency and human control.
From a technical perspective, guided training with partial dependence is implemented by adding a regularization term to the loss function. This term measures the discrepancy between the empirical partial dependence (calculated from the model's predictions) and the objective function provided by the expert. The advantage is that it does not require modifying the network architecture or adding additional layers; the optimization goal is simply modified. In addition, as it is a method based on the average over the dataset, it is robust to noise and does not need the expert to specify relationships at the instance level, but at the population level.
One of the main challenges is the efficient estimation of partial dependency during training, since calculating it over the entire dataset in each iteration would be prohibitive. Current solutions employ subsampling and Monte Carlo techniques to obtain estimates with low bias and controlled variance. In practice, this allows guidance to be integrated without significantly slowing down training, making it viable for real-world applications.
In short, guided training of neural networks through partial dependence opens a promising way to build more interpretable models aligned with domain knowledge. Companies that are committed to artificial intelligence need tools that are not only powerful, but also understandable and controllable. At Q2BSTUDIO, as a software and technology development company, we accompany our customers throughout the life cycle of these solutions: from the conceptualization and design of custom algorithms to integration with cloud infrastructures, visualization with Power BI and security guarantee. If your organization is looking to implement AI models that not only learn, but do it in the right way, the path starts with understanding what knowledge you want to transmit to the machine and how to do it efficiently. Guided partial dependency is one more tool in this journey towards responsible and effective artificial intelligence.
