Why Simple Models Fail: The Easy Trick to Smarter Predictions
Simple models miss hidden patterns—learn how Decision Trees and advanced techniques like Random Forests improve accuracy and make smarter data-driven predictions.
Guessing how something will turn out seems easy says every blackjack player slouched over at 2am in a Vegas casino. It all seems easy, until you actually try it…
But think about trying to figure out how much a house costs or whether someone will miss a loan payment using just one or two details. At first, again it sounds like it should work, but in real life, things are rarely that simple.
Some many different factors come into play to connect in ways that aren’t obvious, and basic machine learning models often miss those deeper patterns.
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So what happens when our models assume things work one way, but reality says otherwise? That’s when things start to break down.
In this article, we’ll look at why simple models don’t always cut it and how better methods, like Decision Trees, can help us make more accurate predictions.
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Alright, let’s start planting some trees to help foster good decisions!
Why Simple Models Fall Short
Take a moment here to imagine you’re trying to predict how much a house costs based only on its size. If price was always tied directly to size, that would work fine.
But in really, it’s not that simple. Things like where the house is, how old it is, what shape it’s in, and how many bedrooms it has all matter too. If we leave those out, our guesses on price will be way off… 2008 off.
Now, if we add in those extra details — like location and number of bedrooms — we’re doing multiple linear regression. But there’s a catch to this: linear models assume that everything has a straight-line relationship. They also think each factor adds to the result without affecting the others.
But what if the price of a big house changes a lot depending on what neighborhood it’s in?
A linear model won’t see that. The same thing happens when we try to predict if someone will default on a loan. If someone makes $50,000 a year, whether or not they already have debt could completely change the risk of default. A simple model might miss that connection.
So, to handle these kinds of situations — where things are more tangled and complicated — we need better models. We need models that can pick up on these hidden relationships without us having to spell them out.
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Decision Trees: Learning Through Questions
Think back to that game we all played as kids—"20 Questions." Someone picks an object, and you ask yes-or-no questions to figure out what it is. Each answer helps you narrow down the possibilities until you land on the right guess.
Decision Trees work the same way. Instead of guessing an object, though, they help us make predictions based on data. Imagine a bank trying to decide if someone should get a loan.
The model might start by asking, "Is their credit score above 650?" If the answer is yes, the next question could be, "Do they have a steady job?" If the answer is no, maybe it asks, "Do they already owe more than $10,000?"
Step by step, the tree breaks people into smaller groups until it reaches a final decision—approve the loan or deny it.
One of the best things about Decision Trees is that they’re easy to understand. You can literally see the logic: the questions it asks, the order it asks them in, and how each answer leads to a decision. That makes them great for explaining predictions in a way people can actually follow.
Beware of these though because a single Decision Tree can be fragile. Even a small change in the data can lead to a completely different tree. That’s why, in practice, we often use Random Forests—which combine multiple trees to make more reliable predictions. But I’ll cover those more at the bottom of this article.
For now, let’s stick with the basics. Think about how you’d decide whether to approve a loan. You’d probably ask a few key yes-or-no questions like, "Is their credit score above 700?" or "Is their debt-to-income ratio under 35%?" That’s exactly what a Decision Tree does—but automatically, and with way more questions if needed.
Here’s a simple way to build a Decision Tree in Python using scikit-learn:
Decision Trees are powerful, but they have a weakness. If you let them grow too deep, they can start memorizing the training data instead of learning real patterns. That means they do great on the data they were trained on but struggle with new data—a problem known as overfitting.
So, while Decision Trees are a great starting point, they’re just one piece of the puzzle. In the article below, I breakdown how we can make them even stronger.
Continue reading the article below for more on Random Forests and XGBoosting ⤵️
Decision Trees, Random Forests, and XGBoost Explained: Supervised Learning for Real-World Problems
Welcome back nerds to our Machine Learning series. So far, we've built a solid starting point. We've learned all about basic models like linear regression and logistic regression, and we’ve gone over how to prepare data and set up pipelines — so we've covered a lot already.
Conclusion
Sometimes, simple models get the job done, but real-world problems are rarely that easy. Take house prices—location, size, age, and condition all play a role. Or loan approvals—income, credit history, and existing debt all matter. When multiple factors interact, basic models struggle to keep up. That’s where Decision Trees come in, breaking things down step by step to make sense of the data.
But as we’ve seen, a single Decision Tree isn’t always reliable. It can be unstable and easily thrown off by small changes in the data. That’s why more advanced methods, like Random Forests, combine multiple trees to improve accuracy and make better predictions.
Bottom line? The best model depends on the problem you’re solving. Simple models can work for straightforward tasks, but when things get messy, you need smarter tools that can recognize hidden patterns. Keep testing, keep improving, and you’ll build models that actually make an impact.
Hope you all have an amazing week nerds ~ Josh
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