Hi Fellow learners and Mentors,

As, I am making progress through the assignments, side by side I am working on building my own implementations based on what I learned so far. In course 3, professor Andrew discussed evaluating the performance of the algorithm using a single value metric on the dev set.

Based on what I understood, I have modelled an implementation for deriving the prediction accuracy, and especially the F1 score.

```
def prediction_accuracy_metric(predicted_labels, true_labels, num_examples):
np.testing.assert_equal(predicted_labels.shape, true_labels.shape)
predicted_labels[predicted_labels > 0.5] = 1
predicted_labels[predicted_labels <= 0.5] = 0
true_positives = np.sum((predicted_labels == 1) & (true_labels == 1))
false_positives = np.sum((predicted_labels == 1) & (true_labels == 0))
false_negatives = np.sum((predicted_labels == 0) & (true_labels == 1))
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_poisitves + false_negatives)
f1 = 2 * (precision * recall) / (precision + recall)
percentage_accuracy = np.sum(predicted_labels == true_labels)/num_examples
return percentage_accuracy, f1
```

Could someone please advise me, if this implementation looks appropriate?

Also, the following is an example of the same from one of the runs of my implementation after making 2500 iterations of optimization.

```
Training set prediction accuracy: 0.9234449760765551, F1 score: 0.8840579710144927
Dev set prediction accuracy: 0.76, F1 score: 0.8181818181818182
```

When I compare the training set and the dev set in terms of only `prediction accuracy`

, I get the idea, that I have a variance problem there. I have overfitted the training set and I should try higher values for regularization parameter or try dropout (which would be a less recommended approach compared to L2 regularization).

But then I look at the `F1 scores`

and get confused because of how close they are. Hence I suspect the correctness of my implementation above.

Could someone please provide me with some advice regarding this ?

Thanks & Regards,

Chandan.