Ever trained an AI model that seemed promising, only to see it crumble in the real world? You're not alone. Many developers fall into common traps like overfitting, thanks to misleading data and unnoticed biases. In clinical terms, most of these issues can be traced back to the data itself.
Data Deception
The root of most machine learning problems is misleading data. This can lead to the infamous 'garbage in, garbage out' scenario, where a model performs well on test data but fails in practice. A prime example was during the Covid pandemic, where numerous prediction models used flawed datasets. These sets often included overlapping records and mislabeling, leading models to learn irrelevant patterns.
Take hidden variables, for instance. These are features in the data that predict class labels without any real-world relevance. When models latch onto them, they might perform well in controlled tests but fail in new scenarios. Remember the Covid chest imaging models that learned patient posture instead of the disease itself? The clearance is for a specific indication. Read the label.
Leaking Information
Data leakage is another silent killer of AI models. Often, it's a result of poor handling of test data, where models inadvertently access information they shouldn't. This happens when pre-processing is applied to the entire dataset before splitting off test data, skewing results. Consider the scenario of centering and scaling: if done prematurely, the model gains unfair insight, inflating its apparent performance.
Forecasting models are particularly prone to this, suffering from look-ahead bias, where future data influences model training. A notorious case involved pre-term birth prediction models, which saw their accuracy plummet once data leaks were corrected.
Mistaken Metrics
Evaluating a model with the wrong metrics can lead to misguided conclusions. Accuracy can be misleading with imbalanced datasets, imagine a model that always predicts the majority class. It might boast high accuracy but offers no real predictive value. Instead, metrics like F score or Matthews correlation coefficient provide a clearer picture.
Time series forecasting amplifies these issues. Many flashy deep learning models, like the autoformer, often underperform compared to simple benchmarks. So why aren't more developers doing the basics right? Perhaps it's the allure of complexity over simplicity.
In light of these challenges, the introduction of checklists like REFORMS is a promising development. It aims to make sure models are built and evaluated correctly, preventing these mistakes. But remember, tools alone won't save you. A healthy dose of skepticism towards your own model is invaluable. Surgeons I've spoken with say it's like trusting a new surgical robot. you check, double-check, and verify before the first incision.
