6.7 Model Evaluation and Performance Metrics
Model evaluation is a process of checking how well a machine learning model performs. It measures the quality of predictions made by the model using test data that was not used during training. This helps in identifying the strengths and weaknesses of the model. Performance metrics are measures used to evaluate how well a model is working. Different metrics are used depending on the type of problem, discussed below:
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lightbulbA model with high accuracy may still give wrong results in some cases. That is why other measures like precision and recall are used. Proper evaluation helps understand the real performance of a model.