What is the Bias-Variance Tradeoff?
Introduction
Machine learning models are developed to infer predictions, aiding decisions based on data. Every data scientist will at some point face the following question from stakeholders:
How do we create models that are both accurate and reliable?
The answer to this question lies in understanding the bias-variance tradeoff, a concept that sits at the heart of machine learning success — and failure.
What is Bias?
Bias refers to errors introduced in the model due to overly simplistic assumptions (e.g. stating all birds can fly, not factoring in penguins). Should your model suffer from high bias, you’re model is underfitting.
Underfitting insinuates that your model is too simple and struggles to capture the underlying pattern in the data. Models that underfit to he training data leading to poor performance on both training and unseen data.
Note: If your model performs poorly, even on training data, you are likely suffering from a bias problem.
What is Variance?
Variance measures how much the model’s predictions change if different training data were used. Should your model suffer from high variance, you’re model is overfitting.
A model that is overfitting is learning the noise in the training data, meaning it performs very well on training data but poorly on new, unseen data.
Note: Overfitting often occurs when working with complex model with many parameters. Error rates are extremely low on training data but high on test data.
The Tradeoff
The bias-variance tradeoff is a major problem in supervised machine learning. Ideally, one would like to choose a model with low bias and low variance, but:
Increasing Model Complexity:
- Reduces bias but increases variance. The model becomes more flexible to fit the training data closely, potentially capturing noise.
Decreasing Model Complexity:
- Reduces variance but increases bias. The model might not capture the underlying trend in the data.
Visualizing the Tradeoff:
The image below shows how model complexity affects bias and variance when training machine learning models.
The key points are:
- Bias decreases as you move right (increasing model complexity).
- Variance increases as you move right.
- The total error (which includes bias, variance, and irreducible error) forms a U-shaped curve, with an optimal point where the model generalizes best.
How can we find the sweet spot?
Model Selection:
- Cross-Validation: Use techniques like k-fold cross-validation to assess how the model performs on unseen data, helping to find the right balance.
- Learning Curves: Generate learning curves to diagnose bias/variance issues.
Feature Engineering:
- Selecting or creating features that capture the true underlying patterns can reduce bias without overly increasing variance.
- Remove noisy or redudant features which can be identified via exploratory data analysis.
Ensemble Methods:
- Methods like Random Forests or Gradient Boosting Machines combine multiple models to balance bias and variance. They can reduce variance by averaging predictions from different models.
Monitoring and Maintenance:
- Track model performance overtime, watch for concept drift and retain your model periodically with new data.
Real-World Example
Consider a model developed to predict housing prices:
- High Bias Example: Using only square footage to predict price might miss out on location, age, or style, leading to poor predictions. Your model will be underfitting to this single feature, resulting in poor inference predictions.
- High Variance Example: A model that includes thousands of features like the exact color of each room might fit the training data perfectly but fail with new unseen houses. Your model will be overfitting to all of the potentially noisy features, resulting in poor inference predicitions.
Conclusion
The bias-variance tradeoff is not about eliminating one or the other but finding the sweet spot where your model can generalize well to new data. Understanding this concept allows data scientists to make informed decisions about model complexity, feature selection, and algorithm choice.
By understanding and actively managing the bias-variance tradeoff, you can build models that don’t just work in theory, but deliver reliable results in the real world.
