Predictive Analytics Modeler Explorer Award Practice Test

The concept of a model fitting the training set perfectly while failing to generalize to unseen future data is referred to as overfitting. When a model is overfitted, it has learned not only the underlying patterns in the training data but also the noise, anomalies, and specific details that do not apply to new data. This results in high accuracy on the training set but significantly poorer performance on any new or unseen data, as the model is too complex and tailored to the training data.

In contrast, underfitting occurs when a model is too simplistic to capture the underlying structure of the data, leading to poor performance on both training and testing datasets. Prediction refers to the act of using a model to estimate or forecast outcomes based on new data, but does not inherently imply a failure in generalizing. Cross-validation is a technique used to assess how well a model generalizes to an independent data set by dividing the data into subsets, training the model on some of them, and validating on others.

Thus, overfitting specifically captures the scenario of perfect training fit with poor performance on future data.