Model Testing
Model testing is the process of evaluating the performance of a machine learning model on a set of data. The purpose of model testing is to determine how well the model is able to generalize to new, unseen data. Model testing involves splitting the available data into training and testing sets. The model is trained on the training set and then evaluated on the testing set. The evaluation metrics used to assess the performance of the model depend on the specific problem being solved. Common evaluation metrics include accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). Model testing is an important step in the machine learning pipeline as it allows the data scientist to assess the performance of the model and make any necessary adjustments before deploying the model in a production environment.
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