Model evaluation is a critical step in the machine learning workflow, as it helps determine how well a model performs and its ability to generalize to unseen data. There are various methodologies for evaluating models, each with its strengths and weaknesses.

One common method is the resubstitution evaluation, where the model is trained and tested on the same dataset. However, this method is not recommended due to the risk of overfitting, as it does not provide a true measure of the model’s generalization ability ². Instead, more robust methods involve partitioning the data into training, validation, and testing sets. This allows for model selection based on performance metrics derived from the validation set, followed by a final assessment on the test set to gauge generalization performance ⁵.

Effective model evaluation can significantly improve model accuracy, transforming a model that predicts with 65% accuracy to one that achieves 80% or 90% ⁴. Metrics such as accuracy, precision, recall, and F1 score are commonly used to quantify model performance.