Confusion matrix logicCalculating a Confusion MatrixPython - Get FP/TP from Confusion Matrix using a ListExport dataset with predicted target - PythonConfusion Matrix - Get Items FP/FN/TP/TN - PythonInterpreting confusion matrix and validation results in convolutional networksHow to make sense of confusion matrixUsing scikit Learn - Neural network to produce ROC CurvesIs it possible to find a model that minimises both false positive and false negative?Confusion MatrixCan Expectation Maximization estimate truth and confusion matrix from multiple noisy sources?
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Confusion matrix logic
Calculating a Confusion MatrixPython - Get FP/TP from Confusion Matrix using a ListExport dataset with predicted target - PythonConfusion Matrix - Get Items FP/FN/TP/TN - PythonInterpreting confusion matrix and validation results in convolutional networksHow to make sense of confusion matrixUsing scikit Learn - Neural network to produce ROC CurvesIs it possible to find a model that minimises both false positive and false negative?Confusion MatrixCan Expectation Maximization estimate truth and confusion matrix from multiple noisy sources?
$begingroup$
Can someone explain me the logic behind the confusion matrix?
- True Positive (TP): prediction is POSITIVE, actual outcome is POSITIVE, result is 'True Positive' - No questions.
- False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
- False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
- True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
confusion-matrix
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Can someone explain me the logic behind the confusion matrix?
- True Positive (TP): prediction is POSITIVE, actual outcome is POSITIVE, result is 'True Positive' - No questions.
- False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
- False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
- True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
confusion-matrix
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
Stick to positive/negative for the test, and True/false for whether the test matches reality (actual outcome). Then it should be clear.
$endgroup$
– Mitch
12 hours ago
add a comment |
$begingroup$
Can someone explain me the logic behind the confusion matrix?
- True Positive (TP): prediction is POSITIVE, actual outcome is POSITIVE, result is 'True Positive' - No questions.
- False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
- False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
- True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
confusion-matrix
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Can someone explain me the logic behind the confusion matrix?
- True Positive (TP): prediction is POSITIVE, actual outcome is POSITIVE, result is 'True Positive' - No questions.
- False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
- False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
- True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
confusion-matrix
confusion-matrix
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
asked 21 hours ago
Tauno TanilasTauno Tanilas
261
261
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Tauno Tanilas is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Stick to positive/negative for the test, and True/false for whether the test matches reality (actual outcome). Then it should be clear.
$endgroup$
– Mitch
12 hours ago
add a comment |
$begingroup$
Stick to positive/negative for the test, and True/false for whether the test matches reality (actual outcome). Then it should be clear.
$endgroup$
– Mitch
12 hours ago
$begingroup$
Stick to positive/negative for the test, and True/false for whether the test matches reality (actual outcome). Then it should be clear.
$endgroup$
– Mitch
12 hours ago
$begingroup$
Stick to positive/negative for the test, and True/false for whether the test matches reality (actual outcome). Then it should be clear.
$endgroup$
– Mitch
12 hours ago
add a comment |
4 Answers
4
active
oldest
votes
$begingroup$
A confusion matrix is a table that is often used to describe the performance of a classification model. The figure you have provided presents a binary case, but it is also used with more than 2 classes (there are just more rows/columns).
The rows refer to the actual Ground-Truth label/class of the input and the columns refer to the prediction provided by the model.
The name of the different cases are taken from the predictor's point of view.
True/False means that the prediction is the same as the ground truth and Negative/Positive refers to what was the prediction.
The 4 different cases in the confusion matrix:
True Positive (TP): The model's prediction is "Positive" and it is the same as the actual ground-truth class, which is "Positive", so this is a True Positive case.
False Negative (FN): The model's prediction is "Negative" and it is wrong because the actual ground-truth class is "Positive", so this is a False Negative case.
False Positive (FP): The model's prediction is "Positive" and it is wrong because the actual ground-truth class is "Negative", so this is a False Positive case.
True Negative (TN): The model's prediction is "Negative" and it is the same as the actual ground-truth class, which is "Negative", so this is a True Negative case.
answered 20 hours ago
Mark.FMark.F
9661418
$endgroup$
2
$begingroup$
Thanks a lot! It's all clear now :)
$endgroup$
– Tauno Tanilas
19 hours ago
add a comment |
$begingroup$
Please find the below:
False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
Answer : The predictive model supposed to give the answer as 'Positive', but it predicted as 'Negative', which means Falsely predicted as Negative aka False Negative.False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
Answer : The predictive model supposed to give the answer as 'Negative', but it predicted as 'Positive', which means Falsely predicted as Positive aka False Positive.True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
Answer : The predicted output supposed to be Negative, and model also predicted as Negative.
For better understanding, you can run a simple binary classfication model and analyze the confusion matrix.
Thank you,
KK
answered 20 hours ago
KK2491KK2491
343219
$endgroup$
add a comment |
$begingroup$
Seems like you understand the meaning of the confusion matrix, nut not the logic used to name its entries!
Here are my 5 cents:
The names are all of this kind:
<True/False> <Positive/Negative>
| |
Part1 Part2
The first part explains if the prediction was right or not. If you have only True Positive and True Negative your model is perfect. If you have only False Positive and False Negative your model is really bad.
The second part explains the prediction of the model.
So:
False Negative (FN): the prediction is NEGATIVE (0) but the first part is False, this means that the prediction is wrong (should have been POSITIVE (1)).
False Positive (FP): the prediction is POSITIVE (1) but the first part is False, this means that the prediction is wrong (should have been NEGATIVE (0)).
True Negative (TN): prediction is NEGATIVE and the first part is True. The prediction is right (model predicted NEGATIVE, for NEGATIVE samples)
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
$endgroup$
add a comment |
$begingroup$
True means Correct, False means Incorrect.
True Positive (TP): Model predicts P, which is Correct.
False Positive (FP): Model predicts P, which is Incorrect, must have predicted N.
True Negative (TN): Model predicts N, which is Correct.
False Negative (FN): Model predicts N, which is Incorrect, must have predicted P.
answered 14 hours ago
EsmailianEsmailian
1,651114
$endgroup$
add a comment |
Your Answer
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4 Answers
4
active
oldest
votes
4 Answers
4
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
A confusion matrix is a table that is often used to describe the performance of a classification model. The figure you have provided presents a binary case, but it is also used with more than 2 classes (there are just more rows/columns).
The rows refer to the actual Ground-Truth label/class of the input and the columns refer to the prediction provided by the model.
The name of the different cases are taken from the predictor's point of view.
True/False means that the prediction is the same as the ground truth and Negative/Positive refers to what was the prediction.
The 4 different cases in the confusion matrix:
True Positive (TP): The model's prediction is "Positive" and it is the same as the actual ground-truth class, which is "Positive", so this is a True Positive case.
False Negative (FN): The model's prediction is "Negative" and it is wrong because the actual ground-truth class is "Positive", so this is a False Negative case.
False Positive (FP): The model's prediction is "Positive" and it is wrong because the actual ground-truth class is "Negative", so this is a False Positive case.
True Negative (TN): The model's prediction is "Negative" and it is the same as the actual ground-truth class, which is "Negative", so this is a True Negative case.
answered 20 hours ago
Mark.FMark.F
9661418
$endgroup$
2
$begingroup$
Thanks a lot! It's all clear now :)
$endgroup$
– Tauno Tanilas
19 hours ago
add a comment |
$begingroup$
A confusion matrix is a table that is often used to describe the performance of a classification model. The figure you have provided presents a binary case, but it is also used with more than 2 classes (there are just more rows/columns).
The rows refer to the actual Ground-Truth label/class of the input and the columns refer to the prediction provided by the model.
The name of the different cases are taken from the predictor's point of view.
True/False means that the prediction is the same as the ground truth and Negative/Positive refers to what was the prediction.
The 4 different cases in the confusion matrix:
True Positive (TP): The model's prediction is "Positive" and it is the same as the actual ground-truth class, which is "Positive", so this is a True Positive case.
False Negative (FN): The model's prediction is "Negative" and it is wrong because the actual ground-truth class is "Positive", so this is a False Negative case.
False Positive (FP): The model's prediction is "Positive" and it is wrong because the actual ground-truth class is "Negative", so this is a False Positive case.
True Negative (TN): The model's prediction is "Negative" and it is the same as the actual ground-truth class, which is "Negative", so this is a True Negative case.
answered 20 hours ago
Mark.FMark.F
9661418
$endgroup$
2
$begingroup$
Thanks a lot! It's all clear now :)
$endgroup$
– Tauno Tanilas
19 hours ago
add a comment |
$begingroup$
A confusion matrix is a table that is often used to describe the performance of a classification model. The figure you have provided presents a binary case, but it is also used with more than 2 classes (there are just more rows/columns).
The rows refer to the actual Ground-Truth label/class of the input and the columns refer to the prediction provided by the model.
The name of the different cases are taken from the predictor's point of view.
True/False means that the prediction is the same as the ground truth and Negative/Positive refers to what was the prediction.
The 4 different cases in the confusion matrix:
True Positive (TP): The model's prediction is "Positive" and it is the same as the actual ground-truth class, which is "Positive", so this is a True Positive case.
False Negative (FN): The model's prediction is "Negative" and it is wrong because the actual ground-truth class is "Positive", so this is a False Negative case.
False Positive (FP): The model's prediction is "Positive" and it is wrong because the actual ground-truth class is "Negative", so this is a False Positive case.
True Negative (TN): The model's prediction is "Negative" and it is the same as the actual ground-truth class, which is "Negative", so this is a True Negative case.
answered 20 hours ago
Mark.FMark.F
9661418
$endgroup$
A confusion matrix is a table that is often used to describe the performance of a classification model. The figure you have provided presents a binary case, but it is also used with more than 2 classes (there are just more rows/columns).
The rows refer to the actual Ground-Truth label/class of the input and the columns refer to the prediction provided by the model.
The name of the different cases are taken from the predictor's point of view.
True/False means that the prediction is the same as the ground truth and Negative/Positive refers to what was the prediction.
The 4 different cases in the confusion matrix:
True Positive (TP): The model's prediction is "Positive" and it is the same as the actual ground-truth class, which is "Positive", so this is a True Positive case.
False Negative (FN): The model's prediction is "Negative" and it is wrong because the actual ground-truth class is "Positive", so this is a False Negative case.
False Positive (FP): The model's prediction is "Positive" and it is wrong because the actual ground-truth class is "Negative", so this is a False Positive case.
True Negative (TN): The model's prediction is "Negative" and it is the same as the actual ground-truth class, which is "Negative", so this is a True Negative case.
answered 20 hours ago
Mark.FMark.F
9661418
answered 20 hours ago
Mark.FMark.F
9661418
answered 20 hours ago
Mark.FMark.F
9661418
answered 20 hours ago
Mark.FMark.F
9661418
9661418
2
$begingroup$
Thanks a lot! It's all clear now :)
$endgroup$
– Tauno Tanilas
19 hours ago
add a comment |
2
$begingroup$
Thanks a lot! It's all clear now :)
$endgroup$
– Tauno Tanilas
19 hours ago
2
2
$begingroup$
Thanks a lot! It's all clear now :)
$endgroup$
– Tauno Tanilas
19 hours ago
$begingroup$
Thanks a lot! It's all clear now :)
$endgroup$
– Tauno Tanilas
19 hours ago
add a comment |
$begingroup$
Please find the below:
False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
Answer : The predictive model supposed to give the answer as 'Positive', but it predicted as 'Negative', which means Falsely predicted as Negative aka False Negative.False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
Answer : The predictive model supposed to give the answer as 'Negative', but it predicted as 'Positive', which means Falsely predicted as Positive aka False Positive.True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
Answer : The predicted output supposed to be Negative, and model also predicted as Negative.
For better understanding, you can run a simple binary classfication model and analyze the confusion matrix.
Thank you,
KK
answered 20 hours ago
KK2491KK2491
343219
$endgroup$
add a comment |
$begingroup$
Please find the below:
False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
Answer : The predictive model supposed to give the answer as 'Positive', but it predicted as 'Negative', which means Falsely predicted as Negative aka False Negative.False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
Answer : The predictive model supposed to give the answer as 'Negative', but it predicted as 'Positive', which means Falsely predicted as Positive aka False Positive.True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
Answer : The predicted output supposed to be Negative, and model also predicted as Negative.
For better understanding, you can run a simple binary classfication model and analyze the confusion matrix.
Thank you,
KK
answered 20 hours ago
KK2491KK2491
343219
$endgroup$
add a comment |
$begingroup$
Please find the below:
False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
Answer : The predictive model supposed to give the answer as 'Positive', but it predicted as 'Negative', which means Falsely predicted as Negative aka False Negative.False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
Answer : The predictive model supposed to give the answer as 'Negative', but it predicted as 'Positive', which means Falsely predicted as Positive aka False Positive.True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
Answer : The predicted output supposed to be Negative, and model also predicted as Negative.
For better understanding, you can run a simple binary classfication model and analyze the confusion matrix.
Thank you,
KK
answered 20 hours ago
KK2491KK2491
343219
$endgroup$
Please find the below:
False Negative (FN): prediction is NEGATIVE, actual outcome is POSITIVE, result is 'False Negative' - Why is that? Shouldn't it be 'False Positive'?
Answer : The predictive model supposed to give the answer as 'Positive', but it predicted as 'Negative', which means Falsely predicted as Negative aka False Negative.False Positive (FP): prediction is POSITIVE, actual outcome is NEGATIVE, result is 'False Positive' - Why is that? Shouldn't it be 'True Negative'?
Answer : The predictive model supposed to give the answer as 'Negative', but it predicted as 'Positive', which means Falsely predicted as Positive aka False Positive.True Negative (TN): prediction is NEGATIVE, actual outcome is NEGATIVE, result is 'True Negative' - Why is that? Shouldn't it be 'False Negative'?
Answer : The predicted output supposed to be Negative, and model also predicted as Negative.
For better understanding, you can run a simple binary classfication model and analyze the confusion matrix.
Thank you,
KK
answered 20 hours ago
KK2491KK2491
343219
answered 20 hours ago
KK2491KK2491
343219
answered 20 hours ago
KK2491KK2491
343219
answered 20 hours ago
KK2491KK2491
343219
343219
add a comment |
add a comment |
$begingroup$
Seems like you understand the meaning of the confusion matrix, nut not the logic used to name its entries!
Here are my 5 cents:
The names are all of this kind:
<True/False> <Positive/Negative>
| |
Part1 Part2
The first part explains if the prediction was right or not. If you have only True Positive and True Negative your model is perfect. If you have only False Positive and False Negative your model is really bad.
The second part explains the prediction of the model.
So:
False Negative (FN): the prediction is NEGATIVE (0) but the first part is False, this means that the prediction is wrong (should have been POSITIVE (1)).
False Positive (FP): the prediction is POSITIVE (1) but the first part is False, this means that the prediction is wrong (should have been NEGATIVE (0)).
True Negative (TN): prediction is NEGATIVE and the first part is True. The prediction is right (model predicted NEGATIVE, for NEGATIVE samples)
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
$endgroup$
add a comment |
$begingroup$
Seems like you understand the meaning of the confusion matrix, nut not the logic used to name its entries!
Here are my 5 cents:
The names are all of this kind:
<True/False> <Positive/Negative>
| |
Part1 Part2
The first part explains if the prediction was right or not. If you have only True Positive and True Negative your model is perfect. If you have only False Positive and False Negative your model is really bad.
The second part explains the prediction of the model.
So:
False Negative (FN): the prediction is NEGATIVE (0) but the first part is False, this means that the prediction is wrong (should have been POSITIVE (1)).
False Positive (FP): the prediction is POSITIVE (1) but the first part is False, this means that the prediction is wrong (should have been NEGATIVE (0)).
True Negative (TN): prediction is NEGATIVE and the first part is True. The prediction is right (model predicted NEGATIVE, for NEGATIVE samples)
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
$endgroup$
add a comment |
$begingroup$
Seems like you understand the meaning of the confusion matrix, nut not the logic used to name its entries!
Here are my 5 cents:
The names are all of this kind:
<True/False> <Positive/Negative>
| |
Part1 Part2
The first part explains if the prediction was right or not. If you have only True Positive and True Negative your model is perfect. If you have only False Positive and False Negative your model is really bad.
The second part explains the prediction of the model.
So:
False Negative (FN): the prediction is NEGATIVE (0) but the first part is False, this means that the prediction is wrong (should have been POSITIVE (1)).
False Positive (FP): the prediction is POSITIVE (1) but the first part is False, this means that the prediction is wrong (should have been NEGATIVE (0)).
True Negative (TN): prediction is NEGATIVE and the first part is True. The prediction is right (model predicted NEGATIVE, for NEGATIVE samples)
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
$endgroup$
Seems like you understand the meaning of the confusion matrix, nut not the logic used to name its entries!
Here are my 5 cents:
The names are all of this kind:
<True/False> <Positive/Negative>
| |
Part1 Part2
The first part explains if the prediction was right or not. If you have only True Positive and True Negative your model is perfect. If you have only False Positive and False Negative your model is really bad.
The second part explains the prediction of the model.
So:
False Negative (FN): the prediction is NEGATIVE (0) but the first part is False, this means that the prediction is wrong (should have been POSITIVE (1)).
False Positive (FP): the prediction is POSITIVE (1) but the first part is False, this means that the prediction is wrong (should have been NEGATIVE (0)).
True Negative (TN): prediction is NEGATIVE and the first part is True. The prediction is right (model predicted NEGATIVE, for NEGATIVE samples)
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
answered 20 hours ago
Francesco PegoraroFrancesco Pegoraro
56717
56717
add a comment |
add a comment |
$begingroup$
True means Correct, False means Incorrect.
True Positive (TP): Model predicts P, which is Correct.
False Positive (FP): Model predicts P, which is Incorrect, must have predicted N.
True Negative (TN): Model predicts N, which is Correct.
False Negative (FN): Model predicts N, which is Incorrect, must have predicted P.
answered 14 hours ago
EsmailianEsmailian
1,651114
$endgroup$
add a comment |
$begingroup$
True means Correct, False means Incorrect.
True Positive (TP): Model predicts P, which is Correct.
False Positive (FP): Model predicts P, which is Incorrect, must have predicted N.
True Negative (TN): Model predicts N, which is Correct.
False Negative (FN): Model predicts N, which is Incorrect, must have predicted P.
answered 14 hours ago
EsmailianEsmailian
1,651114
$endgroup$
add a comment |
$begingroup$
True means Correct, False means Incorrect.
True Positive (TP): Model predicts P, which is Correct.
False Positive (FP): Model predicts P, which is Incorrect, must have predicted N.
True Negative (TN): Model predicts N, which is Correct.
False Negative (FN): Model predicts N, which is Incorrect, must have predicted P.
answered 14 hours ago
EsmailianEsmailian
1,651114
$endgroup$
True means Correct, False means Incorrect.
True Positive (TP): Model predicts P, which is Correct.
False Positive (FP): Model predicts P, which is Incorrect, must have predicted N.
True Negative (TN): Model predicts N, which is Correct.
False Negative (FN): Model predicts N, which is Incorrect, must have predicted P.
answered 14 hours ago
EsmailianEsmailian
1,651114
answered 14 hours ago
EsmailianEsmailian
1,651114
answered 14 hours ago
EsmailianEsmailian
1,651114
answered 14 hours ago
EsmailianEsmailian
1,651114
1,651114
add a comment |
add a comment |
Tauno Tanilas is a new contributor. Be nice, and check out our Code of Conduct.
Tauno Tanilas is a new contributor. Be nice, and check out our Code of Conduct.
Tauno Tanilas is a new contributor. Be nice, and check out our Code of Conduct.
Tauno Tanilas is a new contributor. Be nice, and check out our Code of Conduct.
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Stick to positive/negative for the test, and True/false for whether the test matches reality (actual outcome). Then it should be clear.
$endgroup$
– Mitch
12 hours ago