What is a Confusion Matrix | TP, TN, FP, FN | Type 1 & 2 Error | Easily explained | Machine Learning

[@teklenegash6201]

thank you very much sir. i clearly understand those terms now. could you tell me the reason why testing accuracy, precision, recall become under 10% while the traning and validation accuracy are more than 90%. my confusion matrix value on the precison, recall and f1 score vlues is showing too less which is under 10. here is the final result sir.
Epoch 10/10
164/164 [==============================] - 95s 577ms/step - loss: 0.1356 - accuracy: 0.9502 - val_loss: 0.2857 - val_accuracy: 0.9194
from sklearn.metrics import accuracy_score, confusion_matrix
preds = model.predict(test_data)
acc = accuracy_score(test_labels, np.round(preds))*100
cm = confusion_matrix(test_labels, np.round(preds))
tn, fp, fn, tp = cm.ravel()
print('CONFUSION MATRIX ------------------')
print(cm)
print('
TEST METRICS ----------------------')
precision = tp/(tp+fp)*100
recall = tp/(tp+fn)*100
print('Accuracy: {}%'.format(acc))
print('Precision: {}%'.format(precision))
print('Recall: {}%'.format(recall))
print('F1-score: {}'.format(2*precision*recall/(precision+recall)))
print('
TRAIN METRIC ----------------------')
print('Train acc: {}'.format(np.round((hist.history['accuracy'][-1])*100, 2)))
CONFUSION MATRIX ------------------
[[ 37 197]
[375 15]]
TEST METRICS ----------------------
Accuracy: 8.333333333333332%
Precision: 7.0754716981132075%
Recall: 3.8461538461538463%
F1-score: 4.983388704318937

[@rachittoshniwal]

Why are you doing np.round on preds? They already should be 0s and 1s, right?

[@AT-om6dn]

Is FN value should be more than FP value?

[@chiragsharma9430]

Hi Rachit, where can I find the slides on the confusion matrix and other performance metrics?

[@rachittoshniwal]

Hi Chirag, you can find them on my laptop xD gimme some time, and you'll find them on GitHub too haha

[@chiragsharma9430]

@@rachittoshniwal hahaha take your time and thanks for uploading them.
Really appreciate you taking the time to read my comment and your content you make.

[@rachittoshniwal]

@@chiragsharma9430 oh it's all right!

[@rachittoshniwal]

@@chiragsharma9430 Yo, they're live now. github.com/rachittoshniwal/machineLearning/tree/master/ppts

[@chiragsharma9430]

@@rachittoshniwal yeah I see thanks for uploading them all. These will be helpful while revising things

[@ajinkyaadhotre5336]

hi