You have created a model for fire alarm, explain confusion matrix with this example
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Answers ( 7 )
A model for fire alarm will generally predict if the alarm will ring or not given a certain set of features.
The model will output a confusion matrix which will consists of True Positives (TP), True Negatives (TN),
False Positives (FP) and False Negatives (FN).
Predicted
1 0
____________________
1 | TP | FN |——-> P
Actual |________ |_________|
0 | FP | TN |——–> N
|_________|________ |
True Positive – The model predicted the alarm rings and it actually rings
True Negative – The model predicted the alarm did not ring and it actually did not ring.
False Positive – The model predicted the alarm rings but it actually did not ring.
False Negative – The model predicted the alarm does not ring but it actually rings.
Accuracy is given as
Accuracy = (TP + TN)/(TP +TN + FP +FN) ——–> It shows how often is the classifier correct.
Sensitivity (Recall) = TP/P ——–> When it is actually positive, how often does it predict positive.
Specificity = TN/N ——–> When it is actually negative, how often does it predict negative.
Precision = TP/(TP +FP) ——–> Out of the totally predicted positive, how many are really positive.
F1 Score = ( 2*precision*recall)/(precision + recall)
Sorry it a private answer.
The model for fire alarm will detect if there is a fire or not. The null hypothesis here is that the house is not on fire. The alternative hypothesis is that there is fire.
The confusion matrix will have four values: True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN).
1) TP: There is fire and the alarm rings
2) TN: There is fire but the alarm does not ring (Type I Error)
3) FP: There is no fire and the alarm doesn’t ring
4) FN: There is no fire and the alarm rings (Type II Error)
We can test the accuracy by:
1) Accuracy = (TP + TN)/(TP +TN + FP +FN)
It shows how often is the classifier correct
2) Sensitivity (Recall) = TP/TP+FN
When it is actually positive, how often does it predict positive
3) Specificity = TN/TN + FP
When it is actually negative, how often does it predict negative
4) Precision = TP/(TP +FP)
Out of the total predicted positive, how many are really positive.
5) F1 Score = ( 2*precision*recall)/(precision + recall)
A model for fire alarm means it predicts if the alarm will ring or not during emergencies.
A confusion matrix consists of True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN).
Actual True – The alarm rings
Actual False – The alarm doesn’t ring
Positive – Predicted by model that the alarm rings
Negative – Predicted by model that the alarm doesn’t ring
True Positive – The model predicted that the alarm rings and it actually rings
True Negative – The model predicted the alarm did not ring and it actually did not ring.
False Positive – The model predicted the alarm rings but it actually did not ring.
False Negative – The model predicted the alarm does not ring but it actually rings.
A model for fire alarm will generally predict if the alarm will ring or not given a certain set of features.
The model will output a confusion matrix which will consists of True Positives (TP), True Negatives (TN),
False Positives (FP) and False Negatives (FN).
True Positive – The model predicted the alarm rings and it actually rings
True Negative – The model predicted the alarm did not ring and it actually did not ring.
False Positive – The model predicted the alarm rings but it actually did not ring.
False Negative – The model predicted the alarm does not ring but it actually rings.
Accuracy is given as
Accuracy = (TP + TN)/(TP +TN + FP +FN) ——–> It shows how often is the classifier correct.
Sensitivity (Recall) = TP/TP+FP ——–> When it is actually positive, how often does it predict positive.
Specificity = TN/TN+FN——–> When it is actually negative, how often does it predict negative.
Precision = TP/(TP +FP) ——–> Out of the totally predicted positive, how many are really positive.
F1 Score = ( 2*precision*recall)/(precision + recall)
Fire alarm prediction:
It could only have two outcomes. Either, it could ring or not ring. I will be categorising ring as 1 and not ring as 0.
True positive – It has been predicted that it will ring and actually it rings
True Negative – It has been predicted that it will not ring and in actual it did not ring
False positive – It has been predicted that it will ring but in actual it did not ring. It is also known as type 1 error.
False negative – It has been predicted that it will not ring but in actual it did ring. It is also known as type 2 error.
There are some terms associated with the confusion matrix which are as follows:
Accuracy: Out of all the classes, how much we predicted correctly. ((tp+tn)/(tp+tn+fp+fn))
Recall(r): Out of all the positive classes, how much we predicted correctly. (tp/(tp+fn))
Precision(p): Out of all the positive classes we have predicted correctly, how many are actually positive. (tp/(tp+fp))
F score: F-score helps to measure Recall and Precision at the same time. It uses Harmonic Mean in place of Arithmetic Mean by punishing the extreme values more. ((2*p*r)/(p+r))
Confusion matrix looks like this.