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Hello, seems like the lesson misses explanation on how to solve question #3. I see the solution, but there is no explanation what is what. The way fn_rate formula is constructed is confusing.

#3. Calculate the false negative rate for each threshold

fn_rate = [1 - sum(y_pred_prob[y_test==1][:,1]>t)/pos_cases for t in thresh]

#Find the first threshold value where the is greater than 2 per 100

For anyone struggling with the poor explanation in part 3 of this exercise, I decided to rewrite the code provided in the solution into a function which I think is far more logical, albeit longer, given the context of the lesson so far including my comments on how each step works.

def get_min_thresh(y_pred_prob, min_false_negative_rate):
thresh = np.linspace(0, 1, 100) # creates array between 0 and 1
for t in thresh:
y_pred_class = (y_pred_prob[:,1] > t) * 1.0 # calculates predicted classes for given threshold
confusion = confusion_matrix(y_test, y_pred_class) # computes confusion matrix for given threshold
fn_rate = confusion[1,0] / (confusion[1,0] + confusion[1,1]) # fn_rate = fn / (fn + tp)
if fn_rate >= min_false_negative_rate:
print(f'Minimum Threshold for {min_false_negative_rate*100} false negatives per 100: {t:.2f}')
return t
get_min_thresh(y_pred_prob, 0.02) # returns 0.22 for fn rate of two per hundred positive cases

I hope this is enough until Codecademy update this lesson for more clarity and better understanding.

I’m not sure im getting the y_pred_class = (y_pred_prob[:,1]>0.25) * 1.0
part of this how does that code go through each item in y_pred_prob?
If that’s what its doing how is it then multiplying something like 9.44871111e-04 * 1 and getting 1 or 0