I have trained/fine-tuned a few Keras models and during that, I used 'accuracy' as the only metric to calculate. now after training everything which took a long time, I realized I need precision and recall. Is there a chance I have extract/compute that information after the training is finished with the saved model and the accuracies at hand?

CodePudding user response：

You can apply the trained model on the test set, generate a list of the true labels and predicted labels, and then use the score metrics from scikit-learn. See documentation here:

• https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score
• https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score
• https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html

For example, for a binary classification problem:

from sklearn.metrics import precision_score

# I assume y_true are the list of true labels of the test set
# I assume y_pred are the predictions of the model
precision = precision_score(y_true, y_pred)
recall = recall_score(y_true, y_pred)