Overfitting

TL;DR

A model fits the training data too closely and then performs poorly on unseen data.
Caused by excessive model complexity or tailoring to specific training examples.
Can be mitigated by larger/diverse datasets, regularization (e.g., weight decay, dropout), and cross-validation.

Definition

Overfitting is a phenomenon that occurs when a machine learning model becomes too complex and adapts too closely to the specific training data it was given, resulting in poor generalization to new data. This can lead to poor performance on unseen data and can ultimately hinder the model’s ability to accurately predict outcomes.

Explanation

Overfitting describes a model that has learned patterns specific to the training dataset rather than the underlying relationships that generalize to new inputs. Such a model may show exceptionally good performance on the training set but fail when applied to real-time or previously unseen data. Preventing overfitting involves ensuring the model learns generalizable patterns rather than memorizing training specifics.

Examples

Example 1: Predicting Stock Prices

You train a model on a large amount of historical stock data and observe high accuracy on the training set. When the model is applied to real-time stock prices, it performs poorly. This indicates the model became too complex and learned the specific patterns in the training data too well, failing to generalize to new data.

Example 2: Image Classification

You train a model to classify images of animals (dogs, cats, birds) using a large dataset. The model performs well on the training images but poorly on new images. This shows the model learned the training-data-specific patterns too closely and cannot accurately classify unseen images.

Notes or pitfalls

Overfitting results in poor generalization and degraded performance on unseen data, hindering accurate predictions.
Common strategies to prevent overfitting mentioned here:
- Use a larger and more diverse dataset for training.
- Apply regularization techniques such as weight decay or dropout.
- Use cross-validation to evaluate performance across multiple data splits and detect overfitting.

Generalization
Regularization
Weight decay
Dropout
Cross-validation