In the world of data science, some tools are flashy and new, while others are the foundational workhorses that drive real business value. Chloe Maraina, a business intelligence expert with a remarkable talent for translating complex data into clear, actionable narratives, specializes in the latter. She argues that before we can build complex AI, we must first master the art of finding relationships within our data. Today, we’ll explore the enduring power of regression, diving into how this fundamental machine learning technique is used to forecast trends, optimize operations, and mitigate risk. We’ll discuss the practical differences between various models, the critical balance between model simplicity and accuracy, and the real-world challenges of keeping these predictive tools sharp over time.
A business leader wants to use a simple linear model to link advertising spend directly to sales revenue. How would you explain the core components of this model in simple business terms? Walk us through a scenario where this approach provides valuable, actionable insight for their budget planning.
Of course. This is a classic and powerful application. I’d tell that leader to think of the model, which we often write as y=mx+b, like a simple recipe for sales. The ‘y’ is your total sales revenue—that’s the final dish you want to create. The ‘x’ is your advertising spend, the key ingredient you can control. The most important part is ‘m,’ the coefficient. You can think of ‘m’ as the “bang for your buck.” It tells you exactly how many dollars in sales you can expect for every single dollar you put into advertising. The final piece, ‘b,’ is your baseline. It’s the sales revenue you’d likely get even if you spent nothing on ads, maybe from repeat customers or word-of-mouth. It’s where your sales line starts before the ad money kicks in.
Imagine a scenario where a marketing director is heading into a quarterly budget meeting. There’s pressure to cut costs, but she feels her ad campaigns are working. Instead of just saying “I think this works,” she presents a simple linear regression model. She can stand up and say, “Our model, based on the last two years of data, shows that for every dollar we invest in our web campaign, we generate $3.50 in sales. Our baseline sales are around $50,000 per quarter. If our goal is to hit $200,000 in sales next quarter, we can confidently forecast that we need an ad budget of just over $42,000 to get there.” Suddenly, the conversation shifts from a subjective debate to a data-driven, strategic decision. It’s clear, defensible, and gives the entire team a tool for planning.
Imagine a team is trying to predict a binary outcome, like whether a customer will renew their subscription. What data characteristics would lead you to choose logistic regression for this task? Could you then describe a situation where you might pivot to a polynomial model and why?
For predicting a customer renewal, the first thing I look for is the nature of the question itself. It’s a straight yes-or-no question: Will they renew, or won’t they? That binary outcome is the perfect signal for logistic regression. This model is specifically designed to calculate the probability of one of two outcomes, mapping its output to a value between 0 and 1, which you can think of as the percentage chance of renewal. The data you’d feed it would be factors like how often the customer uses the service, their purchase history, or how long they’ve been a subscriber. The model then learns how these factors together push that probability closer to 1 (likely to renew) or 0 (likely to churn).
Now, let’s say while analyzing the data, we notice something interesting. Our initial linear approach assumed that the more a customer uses the service, the more likely they are to renew, in a straight-line fashion. But we discover that’s not quite right. We see that renewal probability actually peaks for moderately active users, but then slightly decreases for the most hyper-active users, perhaps because they’ve exhausted the service’s value or are power users who are more critical. This relationship isn’t a straight line; it’s a curve. That’s our cue to pivot. We would introduce a polynomial regression model. By allowing the model to fit a curve to the data, we can capture that complex, nonlinear relationship. It acknowledges that the path to renewal isn’t always a simple, straight road, giving us a much more nuanced and accurate prediction.
When a dataset has many highly correlated variables, engineers might use Lasso or Ridge regression. Can you explain the practical difference in how each method handles these variables? Share an example where choosing one over the other significantly impacted the final model’s simplicity and predictive power.
This is a great question because it gets at the heart of model tuning. Both Lasso and Ridge are designed to solve a problem called multicollinearity, which is just a fancy way of saying you have a bunch of input variables that are telling you the same story. For example, in real estate, square footage and the number of bedrooms are often highly correlated.
The practical difference is in their strategy for simplification. Think of Ridge regression as a diplomat. It sees all these correlated variables and decides they all have some value, but it wants to reduce their influence. So, it shrinks their coefficients, pushing them closer and closer to zero but never making them exactly zero. It keeps all the variables in the model, just with less power. Lasso regression, on the other hand, is more like a ruthless editor. It looks at a group of correlated variables and decides to pick a winner. It will shrink the coefficients of the less important variables all the way down to zero, effectively kicking them out of the model entirely.
Let’s imagine we’re building a model to predict customer credit risk, and we have dozens of variables, including ‘total debt,’ ‘number of credit cards,’ and ‘total credit limit’—all highly correlated. If we use Ridge, our final model might include all three variables, each with a small, carefully balanced coefficient. The model could be very accurate but a bit complex to explain. If we use Lasso, the model might decide that ‘total debt’ is the strongest predictor and completely eliminate ‘number of credit cards’ and ‘total credit limit’ from the equation. The result is a much simpler, more interpretable model. This could be a huge win. If the predictive power is nearly the same, a bank would much rather have a simpler model to explain to regulators and use for training, demonstrating clearly which single factor is the most critical. Choosing Lasso in this case would lead to a more streamlined and actionable tool.
Decision tree models are noted for being highly explainable but also prone to overfitting. How do you balance the need for an interpretable model with the risk of poor performance on new data? What specific steps or metrics do you use to mitigate this risk during development?
This is the classic trade-off we face all the time. On one hand, the beauty of a decision tree is its transparency. You can literally draw it out on a whiteboard and walk a business stakeholder through the logic: “If a customer has been with us for more than two years AND they’ve logged in this month, we predict they will renew.” It’s incredibly intuitive. But that same characteristic can be its downfall. A decision tree can keep splitting and creating new rules until it has perfectly memorized every single quirk and bit of noise in your training data. It becomes an expert on the past but is terribly naive about the future, which is the definition of overfitting.
To balance this, my first step is to set limits. I never let a tree grow wild. We enforce constraints, such as limiting the maximum depth of the tree or setting a minimum number of data points required to create a new leaf or decision node. This prevents it from creating hyper-specific rules based on just one or two odd examples. The most crucial technique, however, is to use a method like Random Forest regression. Instead of relying on one, potentially overfitted tree, we build hundreds or even thousands of them. Each tree is trained on a slightly different random subset of the data. To make a final prediction, we let all the trees vote. This “wisdom of the crowd” approach smooths out the biases of any single tree. It dramatically reduces the risk of overfitting and usually leads to a much more robust and accurate model, while still allowing us to inspect individual trees to understand the most important decision-making factors.
In a supply chain context, a regression model is used to estimate product delivery times based on factors like distance, weight, and inventory levels. What are the biggest challenges in maintaining such a model’s accuracy over time, and what is your process for retraining it effectively?
Maintaining a supply chain model is a constant battle against a changing world. The biggest challenge is what we call “concept drift.” The relationships the model learned yesterday might not be true tomorrow. For instance, a sudden spike in fuel costs, which our model uses as a key factor, completely changes the delivery time calculation for long-distance shipments. A new regional warehouse opening up invalidates old assumptions about inventory levels and distance. Even seasonal changes, like holiday rushes or winter weather, can throw the model’s predictions off dramatically if it hasn’t seen that pattern before. The model’s accuracy degrades not because it was built badly, but because the reality it was trying to predict has shifted.
My process for retraining is proactive, not reactive. We don’t wait for delivery times to be wildly off and for customers to start complaining. First, we implement continuous monitoring. We track the model’s prediction error in near real-time, and if it creeps above a certain threshold, it triggers an alert. Second, we have a scheduled retraining cycle, perhaps quarterly, to capture slower, more systemic changes in the business environment. During retraining, we don’t just dump in new data. We carefully curate it, making sure it’s high-quality and reflects the new operational realities. This is also the time to re-evaluate our variables. Is there a new data source we can incorporate? Perhaps real-time traffic data? By treating the model not as a one-time project but as a living system that needs regular check-ups and updates, we ensure it remains a reliable and valuable tool for the business.
What is your forecast for regression in machine learning?
My forecast is that regression will remain the bedrock of practical, value-driven machine learning for the foreseeable future. While a lot of the spotlight is on more complex, “black box” AI, the fundamental need for businesses to understand cause-and-effect relationships is never going away. Leaders will always need to answer questions like, “If we invest here, what will happen over there?” and regression is the most direct, explainable, and powerful tool for answering that. I see its role becoming even more important as a foundational skill. It’s the starting point for more complex analyses and the first tool data scientists reach for to establish a baseline. The future isn’t about replacing regression with something more advanced; it’s about integrating its clear, predictive power into larger, more sophisticated AI systems, ensuring that even our most complex models are grounded in an understandable relationship between input and output. It will continue to be the workhorse that quietly drives countless data-driven decisions.
