What does overfitting refer to in predictive modeling?

Overfitting in predictive modeling occurs when a model is too complex, typically due to incorporating too many variables or parameters relative to the number of observations in the dataset. This complexity allows the model to capture noise and random fluctuations in the training data, rather than the underlying patterns that would be generalizable to new, unseen data.

As a result, an overfitted model will perform exceptionally well on the training dataset but will likely fail to predict accurately on validation or test datasets, as it has essentially memorized the training data rather than learned to make predictions based on relevant trends.

For example, if a model is trained on a small dataset with numerous features, it may learn to recognize specific anomalies or noise rather than the true relationships in the data. This can lead to poor performance when applied to new data where those same anomalies do not occur. Therefore, avoiding overfitting is crucial in predictive modeling, often accomplished by simplifying the model, reducing the number of features, or using techniques such as regularization.

The other choices do not encapsulate the concept of overfitting accurately. Using too few variables might lead to underfitting, where the model fails to capture relevant information. Focusing on outlier detection is a different problem related to