Evaluating my model in new unseen dataset

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I have trained my model (.fit()) and satisfied with the performance on test split making prediction (.predict()). So I save my model to the disk (.save('model.h5')).

Now I'm given new unseen dataset and asked to evaluate my already saved model on this dataset for performance. I am required to not only report accuracy but stuff line precision/recall, confusion matrix etc...

I then loaded my saved model (.load_model('model.h5')).

Question: What the appropriate function that I should use to prepare report of the model performance on this new dataset. I should I use .predict() function or .evaluate().

keras deep-learning machine-learning python-3.x python
2 Answers

If you want to get loss/accuracy or whatever other metrics you had during training - you need .evaluate() method. If all you need is actual probabilities or regression values - you need .predict() method.

user_39295410

You can use sklearn's classification_report to generate all the relevant metrics.
Code:

preds = model.predict(x1)
y_pred = np.argmax(preds, axis = 1)
print(classification_report(y1, y_pred))

Output:

              precision    recall  f1-score   support

           0       0.71      0.08      0.15        59
           1       0.42      0.95      0.58        41

    accuracy                           0.44       100
   macro avg       0.57      0.52      0.37       100
weighted avg       0.59      0.44      0.33       100

You can see all available metrics here.

model.evaluate() :
This method will provide the values for loss and metrics you defined in your compile method which is generally not enough.

user_95579700
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