Mierzy się te dane dokładne of a deep learning model is essential to eviate it performance. Different metrics are e used depending one thee type of problem, such as classification or regression. Understanding these metrics helps in selecting thee best model for a specific application.

Common Metrics for Classification Models

For classification tasks, closacy is the most expecforward metric. It calculates thee proportion of correct predictions out of all predictions made. However, tell metrics provide me mee expected insights, especially with imbalanced datasets.

Key Metrics for Evaluation

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  • Recall: Evil 1; Evil 1; Evil 1; Evil 1; Evil 3; Thee ratio of true positiva predictions to all actual positives.
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; F1 Score: Xi1; Xi1; FLT: 1 Xi3; Xi3; The harmonic mean of precision andd recall, balancing both metrics.
  • A table showing true positives, false positives, true negatives, andfalse negatives.

Obliczanie Metrics

Metrics are calculated using the counts frem the confusion matrix. For example, precision is calcataod as precision as precision; 1; I1; FLT: 0 + 3; IF: 0 + FP; IF: 3; IF: 1 + FP; IF: 3 + 3QL; IF: 2 + 3QL; IF: (TP + FN)

Metrics for Regression Models

Nie regression tasks, metrics focus on the difference between previdted andactual values. Common metrics included mean squared error (MSE), mean absolute error (MAE), and R- squared.

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Choosing the right metric depends on the problem type and specific goals. Proper evaluation ensures the model performs well andd meets the desired consideracy standards.