In machine learning, when a statistical model describes random error or noise instead of underlying relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally observed, because of having too many parameters with respect to the number of training data types. The model exhibits poor performance which has been overfit.

In machine learning, ‘overfitting’ refers to a scenario where a model learns the training data too well, capturing noise or random fluctuations in the data as if they were genuine patterns. As a result, the model performs very well on the training data but fails to generalize well to unseen data (i.e., performs poorly on new, unseen examples).

Overfitting typically occurs when a model is excessively complex relative to the amount and quality of the training data available. The model essentially memorizes the training data instead of learning the underlying patterns that would enable it to make accurate predictions on new data. This phenomenon can be visualized in a graph where the model’s performance on the training data continues to improve as the complexity increases, but the performance on the validation or test data starts to degrade after a certain point.

To mitigate overfitting, various techniques can be employed, including:

Simplifying the model: Using a less complex model with fewer parameters can help prevent overfitting.
Regularization: Adding regularization terms to the model’s objective function penalizes overly complex models, discouraging them from fitting noise.
Cross-validation: Dividing the data into multiple subsets for training and validation can help assess the model’s generalization performance and identify overfitting.
Feature selection/reduction: Removing irrelevant features or reducing the dimensionality of the feature space can help prevent the model from fitting noise in the data.
Early stopping: Monitoring the model’s performance on a validation set during training and stopping the training process when the performance starts to degrade can prevent the model from overfitting.
Ensemble methods: Combining multiple models (e.g., through techniques like bagging or boosting) can help reduce overfitting by leveraging the wisdom of crowds and smoothing out individual model biases.

Understanding and addressing overfitting is crucial in machine learning to ensure that models generalize well to new, unseen data and can make reliable predictions in real-world applications.