Overfitting in Machine Learning

Summary

Overfitting in machine learning occurs when a model captures not only the underlying patterns in the training data but also the noise, leading to poor performance on unseen data, and is unable to generalise.

Mathematically, overfitting results in a model with low bias but high variance, meaning it adapts too closely to the training data and fails to generalize well.

Key methods to address overfitting include regularisation (such as $L_1$ and $L_2$ regularization), Cross Validation, and simpler models.

In statistical terms, it indicates a model with high complexity and too many parameters relative to the amount of training data, which results in $f(x)$ poorly representing the population distribution.

Breakdown

Key Components:

• Regularization (Lasso: $L_1$, Ridge: $L_2$) to penalize model complexity.
• Cross Validation to ensure model generalization.
• Early stopping in training to avoid learning noise.
• Simplification of models to prevent fitting irrelevant patterns.

Important

• Overfitting indicates high variance in the model’s performance, which can be identified by a significant drop in accuracy between training and test datasets.
• Regularization adds penalty terms to the cost function, reducing model complexity and mitigating overfitting.

Attention

• Overfitting is more common in models with high-dimensional datasets.
• Excessive model tuning (hyperparameter optimization) may inadvertently increase overfitting.

Follow

• How does the choice of regularization type (e.g., $L_1$ vs. $L_2$) affect model generalization in overfitting scenarios?
• What role does the size of the training dataset play in mitigating overfitting?

Related

• Cross Validation techniques (e.g., $k$-fold, Leave-One-Out cross-validation)
• Bias and varianceradeoff in machine learning models