In machine learning, overfitting occurs when a model is overly complex and overly trained on a specific dataset, resulting in poor performance on unseen data. Overfitting can be caused by several factors. One common cause is having too many features compared to the amount of available data. When the number of features exceeds the number of data points, the model can start to memorize the training examples, leading to overfitting.

Another factor that contributes to overfitting is using a model that is too complex. Complex models, such as deep neural networks with many layers, have a high capacity to fit the training data but may struggle to generalize to new, unseen data.

To identify overfitting, several techniques can be used. One approach is to split the available data into training and validation sets. The model is trained on the training set and evaluated on the validation set. If the model performs significantly worse on the validation set compared to the training set, it is likely overfitting.

Other common indicators of overfitting include excessively high accuracy on the training data, but low accuracy on test or validation data. An overfitted model may also exhibit large discrepancies between its performance measures on training and validation data.

To mitigate overfitting, various techniques can be employed. One such technique is regularization, which adds a penalty term to the model's error function to discourage complex and overparameterized solutions. Feature selection can also help by reducing the number of input features to only the most relevant ones for the problem at hand. Additionally, cross-validation techniques, like k-fold cross-validation, can provide a more robust estimate of the model's performance.

By understanding the causes and identifying overfitting in machine learning models, we can apply the appropriate techniques to achieve better generalization and more reliable model predictions. Remember to strike the balance between complexity and simplicity for optimal model performance!