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 basic 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 any unseen data for a fair assessment of generalization accuracy ².

A more robust approach involves partitioning the data into training, validation, and testing sets. The model is trained on the training set, tuned using the validation set, and finally evaluated on the test set to assess its generalization performance ⁵. This method helps ensure that the model selected is the best performer on new data.

Additionally, metrics such as accuracy, precision, recall, and F1 score can be used to quantify model performance, allowing for comparisons between different models ⁴. Understanding these evaluation techniques is essential for improving model performance and making informed decisions in machine learning projects.