Overfitting occurs when a model performs well on training data but poorly on unseen data.Group of answer choicesTrueFalse

The statement is True. Overfitting is a common issue in machine learning and statistical modeling where a model learns the noise or random fluctuations in the training data instead of the actual underlying patterns. This results in the model being overly complex, capturing every detail in the training set, and consequently, it fails to generalize to new, unseen data. Consequently, while the model may achieve high accuracy during training, its performance deteriorates significantly when making predictions on new data. This imbalance can lead to poor predictive performance outside the training environment, indicating that the model is not effectively learned and may need regularization or simpler features to improve its generalization capabilities.