Episode summary

In Episode 33 of '100 Days of Data,' Jonas and Amy dive deep into the critical concepts of overfitting and underfitting in AI models. Using relatable analogies and real-world examples—from predictive maintenance to customer churn—they explain how models can be too simplistic or overly complex, both of which hurt performance. The discussion introduces key concepts like generalization, bias, and variance, emphasizing that the goal is to find the right balance for your model to perform well on new, unseen data. They explore practical approaches such as regularization, cross-validation, and appropriate model selection to manage complexity. Whether you're a data scientist or business leader, this episode offers valuable insights on how to spot and fix model performance issues before they impact outcomes.

Episode video

Episode transcript

JONAS: Welcome to Episode 33 of 100 Days of Data. I'm Jonas, an AI professor here to explore the foundations of data in AI with you.
AMY: And I, Amy, an AI consultant, excited to bring these concepts to life with stories and practical insights. Glad you're joining us.
JONAS: Why can an AI model be too dumb or too smart? That’s the puzzle we’re tackling today with overfitting and underfitting.
AMY: It’s like Goldilocks and the Three Bears—your model needs to be just right. Too simple, and it misses signals. Too complex, and it gets tricked by noise. But what does that really mean in practice?
JONAS: Let’s start with the basics. When we talk about overfitting and underfitting, we’re discussing how well a model generalizes. In AI, generalization means the model’s ability to make accurate predictions on new, unseen data—not just the data it was trained on.
AMY: Right, because in business, the whole point is that your AI helps with future decisions or new customers, not just past examples.
JONAS: Exactly. Underfitting occurs when a model is too simple to capture the underlying patterns in the data. It performs poorly on both training data and new data because it hasn’t learned enough.
AMY: Think of a car dealership using a linear model to predict sales based only on advertising spend, ignoring other vital factors like seasonality or economic conditions. The predictions would be off and not useful.
JONAS: On the flip side, overfitting happens when the model is too complex. It learns the training data too well, including the random fluctuations or noise, mistaking them for real patterns.
AMY: I’ve seen this with predictive maintenance projects in manufacturing. A team uses a highly complex model that nails every little quirk in the historical sensor data but then fails miserably when actual machines behave differently.
JONAS: Conceptually, overfitting is like memorizing a textbook without understanding. You can recite every example, but on test day with new problems, you falter.
AMY: That’s a perfect analogy. In contrast, underfitting is like skimming the book—you don’t learn enough to answer even the questions you practiced.
JONAS: These ideas lead us to an essential concept in AI: the bias-variance tradeoff. Bias refers to errors from assumptions in the learning algorithm—high bias means the model is too rigid or simple, causing underfitting.
AMY: And variance relates to how sensitive the model is to small fluctuations in the training data. A high-variance model reacts too much to noise, meaning it overfits.
JONAS: So, managing the tradeoff between bias and variance is the art of building effective AI models. You want a low-bias, low-variance model to generalize well.
AMY: In real-world terms, data scientists often use techniques like cross-validation or regularization to find that balance. Regularization, for example, adds a penalty for complexity to discourage overfitting.
JONAS: Another practical approach is controlling model complexity by choosing appropriate algorithms. For instance, a linear regression might underfit a problem if the relationship is non-linear, while a deep neural network might overfit if hyperparameters aren’t tuned properly.
AMY: That reminds me of a retail client working on customer churn prediction. They started with a simple decision tree model—it underperformed, missing patterns. Then they shifted to a more complex random forest but saw the model struggling with new customers because it was too tailored to old data.
JONAS: It’s a classic case. One solution is pruning the tree or limiting depth—reducing complexity to avoid overfitting.
AMY: And in healthcare, I’ve seen imaging AI systems overfitting to training images from one hospital and failing on others because the training set didn’t represent the diversity of patients or equipment variations. This is a reminder data quality and variety matter a lot.
JONAS: Indeed. Having diverse, representative training data helps reduce both overfitting and underfitting by supporting better generalization.
AMY: So, if you’re a manager or consultant, how do you spot these issues when an AI project is underway? What signs reveal that the model might be overfitting or underfitting?
JONAS: One clue comes from performance metrics. If your model performs brilliantly on training data but poorly on validation or test data, that’s overfitting. If it performs poorly on both, think underfitting.
AMY: I also encourage teams to visualize the model’s predictions versus actual outcomes. Sometimes patterns jump out that numbers alone don’t show.
JONAS: And understanding what bias and variance mean in your specific context can inform how to adjust model complexity or gather more data.
AMY: Let’s talk about generalization error—that’s the gap between the model’s performance on training data and unseen data. Minimizing this error is the goal.
JONAS: Exactly. We use techniques like holdout testing or k-fold cross-validation to estimate generalization error accurately.
AMY: To wrap it all up with a practical example, consider credit scoring in banking. A model too simple won’t flag risky borrowers effectively—that’s underfitting and leads to losses. A model too complex might catch random quirks in past applicants, falsely labeling good borrowers as risky—that’s overfitting, leading to lost customers.
JONAS: That example shows why balancing bias and variance isn’t just academic—it’s vital to business outcomes.
AMY: Plus, this balance affects trust and usability. If a model feels inconsistent or unfair, stakeholders lose confidence quickly.
JONAS: So, some final tips: always evaluate your models on new data, choose the right complexity, gather diverse data, and understand your tradeoffs.
AMY: And keep communicating what’s possible—and what’s not—to your teams and leaders. AI isn’t magic; it’s about making good guesses based on data.
JONAS: Key takeaway—underfitting means the model is too simple to capture data patterns; overfitting means it’s too complex, capturing noise instead of signal.
AMY: And from my side, always look for signs in real-world results. If your AI’s decisions don’t hold up beyond the training data, it’s time to rethink your model’s complexity or data.
JONAS: Next episode, we’ll dig into bias in AI—how unfair assumptions creep in and what we can do about them.
AMY: If you're enjoying this, please like or rate us five stars in your podcast app. We’d love to hear your questions or comments that might shape future episodes.
AMY: Until tomorrow — stay curious, stay data-driven.

Next up

Next time, explore how hidden biases can creep into AI systems—and what you can do to prevent unfair outcomes.