What is Overfitting?
Overfitting is a modeling error in statistics that occurs when a function is excessively aligned to a specific set of data points. While this tailored approach can initially show high accuracy, the model becomes unreliable when applied to new, unseen data since it is essentially ‘memorizing’ the training set, including noise and outliers, rather than finding a generalize-able pattern.
Overfitting often manifests as an overly complex model tailored to every small fluctuation in the data. By precisely fitting its details, the model incorporates noise and anomalies, thereby reducing its predictive power on new datasets.
Key Takeaways
- Overfitting occurs when a model too closely aligns to a small dataset, losing predictive reliability on new data.
- Financial and data professionals risk flawed results by overfitting models based on limited data.
- Compromised models lost as predictive tools can lead to erroneous decisions and investments.
- Simplicity in design can prevent an overly complex model that doesn’t generalize well to new data.
- Overfitting happens more frequently than underfitting but can be controlled by mitigating model complexity and ensuring broad data coverage.
Understanding Overfitting
For instance, consider using computer algorithms to find patterns in extensive historical market data. It’s feasible to create elaborate models that predict stock returns with almost foolproof precision. However, these models often fail when applied to data outside the sample, exposing themselves as merely overfit to random fluctuations. Successful models should always validate against separate data to ensure reliability and general applicability.
How to Prevent Overfitting
Preventing overfitting involves strategies like cross-validation, where the dataset is split into partitions, and the model is tested in segments to ensure generality. Ensembling gathers predictive insights from multiple models for a balanced approach, and data augmentation increases dataset diversity for comprehensive modeling. Data simplification—reducing the model’s complexity by eliminating redundant features—also helps avoid overfitting. Achieving the right balance between complexity and simplicity is key, preventing technical debt and maintaining broad predictive power.
Overfitting in Machine Learning
In machine learning, overfitting arises when a machine learning algorithm is too specific in recognizing patterns in training data but performs poorly on new data. This misstep often results from high variance and low bias, causing a breakdown in model reliability. By contrast, effective model-building should maintain high accuracy on both known and unknown data, blending generalized patterns rather than isolated specifics.
Overfitting vs. Underfitting
Overfitting and underfitting are two sides of the same issue—a compromise between specificity and generality. An overfitted model is overly complex with low bias and high variance, capturing noise and anomalies, while an underfitted model retains high bias and low variance due to its simplicity and insufficient training complexity. Balancing these extremes by dynamically tuning model readiness and appropriately developing training data breadth gives rise to solid, practical models.
Overfitting Example
Consider a university aiming to reduce its college dropout rate by predicting graduate likelihood based on applicant data. They trained a model with data from 5,000 past applicants and found a 98% success rate using that same dataset. However, applying the model to the next 5,000 applicants resulted in only 50% accuracy. This drastic difference highlights overfitting, showing that the model was unduly specialized on outlier data from the initial set, leading to ineffective predictions.
Related Terms: cross-validation, machine learning, predictive modeling, data augmentation, bias, variance.
