Evaluation Metrics
In this step, Evaluation Metrics are defined to measure the performance of the model. This involves selecting key indicators that will be used to assess the effectiveness of the algorithm in achieving its objectives. The metrics chosen should be relevant and align with the project's goals, such as accuracy, precision, recall, F1 score, mean squared error, or R-squared value, depending on the type of problem being addressed. Additionally, evaluation metrics may include statistical measures like standard deviation, variance, or correlation coefficients to provide a comprehensive understanding of model performance. The selection of suitable metrics is crucial as it will guide further refinements and improve the overall quality of the model.