What Is Overfitting?
Let me explain overfitting to you directly: it's a modeling error in statistics that happens when a function aligns too closely with a limited set of data points. As a result, the model only works well for that initial dataset and fails with any others.
When you overfit a model, you're essentially creating something overly complex to account for quirks in the data you're studying. Real data often includes errors or random noise, so trying to fit the model too tightly to imperfect data introduces big errors and weakens its ability to predict accurately.
Key Takeaways
- Overfitting is an error in data modeling from a function fitting too closely to a small set of data points.
- Financial professionals risk overfitting models on limited data, leading to flawed results.
- An overfitted model loses its value as a predictive tool for investing.
- Models can also be underfitted, meaning they're too simple with too few data points to be effective.
- Overfitting is more common than underfitting and often stems from efforts to avoid underfitting.
Understanding Overfitting
Consider this example: a common issue arises when using algorithms to sift through vast databases of historical market data to spot patterns. With enough analysis, you can craft detailed theories that seem to predict stock market returns with high accuracy.
But when you apply these theories to data beyond the original sample, they often turn out to be just overfitting to random chance events. That's why you must always test your model on data outside the development sample.
How to Prevent Overfitting
You can prevent overfitting through several methods. One is cross-validation, where you divide the training data into folds or partitions, run the model on each, and average the error estimates. Other approaches include ensembling, combining predictions from at least two models; data augmentation, making your dataset appear more diverse; and data simplification, streamlining the model to avoid excess complexity.
Important Note
As a financial professional, you need to stay vigilant about the risks of overfitting or underfitting models with limited data. Aim for a balanced model that's neither too complex nor too simple.
Overfitting in Machine Learning
Overfitting also appears in machine learning. It can occur when a machine is trained to detect specific data in one way, but applying the same process to new data yields wrong results. This stems from model errors, typically showing low bias and high variance. Redundant or overlapping features might make the model unnecessarily complicated and ineffective.
Overfitting vs. Underfitting
An overfitted model is too complicated, rendering it ineffective. Conversely, an underfitted model is too simple, lacking enough features and data to work well. Overfitting features low bias and high variance, while underfitting has high bias and low variance. To reduce bias in a simple model, add more features.
Overfitting Example
Take this scenario: a university facing a higher-than-desired dropout rate wants to build a model predicting if applicants will graduate.
They train the model on a dataset of 5,000 applicants and their outcomes. Running it back on that same dataset gives 98% accuracy. But testing on a second set of 5,000 applicants drops accuracy to 50%, because the model was overfitted to the narrow first dataset.
