Ocena wyników tych działań, które są przedmiotem zainteresowania, pozwala na porównanie modeli i optymalizacji ich wyników, a także ich wyników, które są specyficzne dla zadań.

Common Performance Metrics

Several metrics are used tone asses deep learning models, especially in classification and regression tasks. These metrics quantify how well a model predicts or fits the data.

Ocena Metrics for Classification

For classification tasks, combine metrics include closiety, precision, recall, and F1 score. These metrics evaluate different aspects of thee model 's previsitiva ability.

Dokładność

Dokładne pomiary te proporcje korekcji przewidywania są poza prognozami totalnymi.

Precision andd Recall

Precyzyjny wskaźnik ten proporcjos of true positiva prognoses among all positive predictions, while e recall measures the proportion of actualt positives correctly identified.

F1 Score

Te F1 score combinas precision and recall into a single metric, provising a balanced measure especially when classes are imbalanced.

Evaluation Metrics for Regression

Regression models are e eviated using metrics that metrice thee between previdted andactual values. Common metrics included Mean Absolute Error (MAE), Mean Squared Error (MSE), and R- squared.

Mean Absolute Error (MAE)

MAE calculates thee average absolute difference ce between predned andd true values, indicating thee average predtion error.

Mean Squared Error (MSE)

MSE measures the average squared difference, penalizing larger errors more heavily than MAE.

R- squared

R- squared indicates the proportion of variance in thee data explained by they model, with values closer to 1 presenting better fit.

Cross- Validation Techniques

Cross- validation methods, such as k- fold cross- validation, help assess the generalization ability of models by partitioning data into training and testing sets multiple times.

This approach reduces overfitting andd provides a more reliable estimate of model performance across different data subsets.