Assessment
Assessment is the process of evaluating the performance of a model or algorithm based on a set of predefined metrics. It is a crucial step in the machine learning pipeline as it helps to determine the accuracy and effectiveness of the model. The assessment process involves splitting the data into training and testing sets, training the model on the training set, and then evaluating its performance on the testing set. The metrics used for assessment can vary depending on the problem being solved, but commonly used metrics include accuracy, precision, recall, F1 score, and area under the curve (AUC). Assessment can also involve comparing the performance of different models or algorithms to determine which one is the most effective for a given problem.
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