Select two best classifier using F1-score,Recall and precision
I have three classifiers that classify same dataset with these results:
classifier A:
precision recall f1-score
micro avg 0.36 0.36 0.36
macro avg 0.38 0.43 0.36
weighted avg 0.36 0.36 0.32
classifier B:
precision recall f1-score
micro avg 0.55 0.55 0.55
macro avg 0.60 0.60 0.56
weighted avg 0.61 0.55 0.53
classifier C:
precision recall f1-score
micro avg 0.34 0.34 0.34
macro avg 0.36 0.38 0.32
weighted avg 0.39 0.34 0.32
I want two select two best of them, and I know F1-score is a parameter for compare the classifiers because of its harmony between precision and recall.
So, at first I select classifier B for its best F1-score. for next, both A and C have a same F1-measure,
I want to ask how can I select between them?
machine-learning python classification
asked Dec 28 '18 at 11:47
Saha
266
add a comment |
I have three classifiers that classify same dataset with these results:
classifier A:
precision recall f1-score
micro avg 0.36 0.36 0.36
macro avg 0.38 0.43 0.36
weighted avg 0.36 0.36 0.32
classifier B:
precision recall f1-score
micro avg 0.55 0.55 0.55
macro avg 0.60 0.60 0.56
weighted avg 0.61 0.55 0.53
classifier C:
precision recall f1-score
micro avg 0.34 0.34 0.34
macro avg 0.36 0.38 0.32
weighted avg 0.39 0.34 0.32
I want two select two best of them, and I know F1-score is a parameter for compare the classifiers because of its harmony between precision and recall.
So, at first I select classifier B for its best F1-score. for next, both A and C have a same F1-measure,
I want to ask how can I select between them?
machine-learning python classification
asked Dec 28 '18 at 11:47
Saha
266
Why is accuracy not the best measure for assessing classification models? Everything in that thread applies equally to F1, recall and precision. See also Classification probability threshold.
– Stephan Kolassa
Dec 28 '18 at 13:31
add a comment |
I have three classifiers that classify same dataset with these results:
classifier A:
precision recall f1-score
micro avg 0.36 0.36 0.36
macro avg 0.38 0.43 0.36
weighted avg 0.36 0.36 0.32
classifier B:
precision recall f1-score
micro avg 0.55 0.55 0.55
macro avg 0.60 0.60 0.56
weighted avg 0.61 0.55 0.53
classifier C:
precision recall f1-score
micro avg 0.34 0.34 0.34
macro avg 0.36 0.38 0.32
weighted avg 0.39 0.34 0.32
I want two select two best of them, and I know F1-score is a parameter for compare the classifiers because of its harmony between precision and recall.
So, at first I select classifier B for its best F1-score. for next, both A and C have a same F1-measure,
I want to ask how can I select between them?
machine-learning python classification
asked Dec 28 '18 at 11:47
Saha
266
I have three classifiers that classify same dataset with these results:
classifier A:
precision recall f1-score
micro avg 0.36 0.36 0.36
macro avg 0.38 0.43 0.36
weighted avg 0.36 0.36 0.32
classifier B:
precision recall f1-score
micro avg 0.55 0.55 0.55
macro avg 0.60 0.60 0.56
weighted avg 0.61 0.55 0.53
classifier C:
precision recall f1-score
micro avg 0.34 0.34 0.34
macro avg 0.36 0.38 0.32
weighted avg 0.39 0.34 0.32
I want two select two best of them, and I know F1-score is a parameter for compare the classifiers because of its harmony between precision and recall.
So, at first I select classifier B for its best F1-score. for next, both A and C have a same F1-measure,
I want to ask how can I select between them?
machine-learning python classification
machine-learning python classification
asked Dec 28 '18 at 11:47
Saha
266
asked Dec 28 '18 at 11:47
Saha
266
asked Dec 28 '18 at 11:47
Saha
266
asked Dec 28 '18 at 11:47
Saha
266
asked Dec 28 '18 at 11:47
Saha
266
266
Why is accuracy not the best measure for assessing classification models? Everything in that thread applies equally to F1, recall and precision. See also Classification probability threshold.
– Stephan Kolassa
Dec 28 '18 at 13:31
add a comment |
Why is accuracy not the best measure for assessing classification models? Everything in that thread applies equally to F1, recall and precision. See also Classification probability threshold.
– Stephan Kolassa
Dec 28 '18 at 13:31
Why is accuracy not the best measure for assessing classification models? Everything in that thread applies equally to F1, recall and precision. See also Classification probability threshold.
– Stephan Kolassa
Dec 28 '18 at 13:31
Why is accuracy not the best measure for assessing classification models? Everything in that thread applies equally to F1, recall and precision. See also Classification probability threshold.
– Stephan Kolassa
Dec 28 '18 at 13:31
add a comment |
2 Answers
2
active
oldest
votes
f1-score combines precision and recall in a single figure. As both are pretty similar in A and C cases, f1-score is similar too.
Your choice depends on what it is less harmful in your categorization: false positives or false negatives.
I do recommend you to read the 3rd chapter of "DEEP LEARNING:From Basics to Practice" volume 1 by Andrew Glassner. There you have the three concepts (precision, recall and f1-score) described in a very illustrative way.
answered Dec 28 '18 at 12:03
David
562
they are not comparable if we dont have FP or FN?
– Saha
Dec 28 '18 at 12:07
You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice.
– David
Dec 29 '18 at 8:16
add a comment |
It depends on your application. Assume that you design a classifier model to predict whether a person has cancer. If you wanna say confidently that a person has cancer, you probably prefer a classifier with high precision.
On the other hand, if you want to make sure all people with cancer will be caught, you probably prefer a classifier with high recall.
answered Dec 28 '18 at 13:38
Nga Dao
1495
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
f1-score combines precision and recall in a single figure. As both are pretty similar in A and C cases, f1-score is similar too.
Your choice depends on what it is less harmful in your categorization: false positives or false negatives.
I do recommend you to read the 3rd chapter of "DEEP LEARNING:From Basics to Practice" volume 1 by Andrew Glassner. There you have the three concepts (precision, recall and f1-score) described in a very illustrative way.
answered Dec 28 '18 at 12:03
David
562
they are not comparable if we dont have FP or FN?
– Saha
Dec 28 '18 at 12:07
You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice.
– David
Dec 29 '18 at 8:16
add a comment |
f1-score combines precision and recall in a single figure. As both are pretty similar in A and C cases, f1-score is similar too.
Your choice depends on what it is less harmful in your categorization: false positives or false negatives.
I do recommend you to read the 3rd chapter of "DEEP LEARNING:From Basics to Practice" volume 1 by Andrew Glassner. There you have the three concepts (precision, recall and f1-score) described in a very illustrative way.
answered Dec 28 '18 at 12:03
David
562
they are not comparable if we dont have FP or FN?
– Saha
Dec 28 '18 at 12:07
You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice.
– David
Dec 29 '18 at 8:16
add a comment |
f1-score combines precision and recall in a single figure. As both are pretty similar in A and C cases, f1-score is similar too.
Your choice depends on what it is less harmful in your categorization: false positives or false negatives.
I do recommend you to read the 3rd chapter of "DEEP LEARNING:From Basics to Practice" volume 1 by Andrew Glassner. There you have the three concepts (precision, recall and f1-score) described in a very illustrative way.
answered Dec 28 '18 at 12:03
David
562
f1-score combines precision and recall in a single figure. As both are pretty similar in A and C cases, f1-score is similar too.
Your choice depends on what it is less harmful in your categorization: false positives or false negatives.
I do recommend you to read the 3rd chapter of "DEEP LEARNING:From Basics to Practice" volume 1 by Andrew Glassner. There you have the three concepts (precision, recall and f1-score) described in a very illustrative way.
answered Dec 28 '18 at 12:03
David
562
answered Dec 28 '18 at 12:03
David
562
answered Dec 28 '18 at 12:03
David
562
answered Dec 28 '18 at 12:03
David
562
562
they are not comparable if we dont have FP or FN?
– Saha
Dec 28 '18 at 12:07
You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice.
– David
Dec 29 '18 at 8:16
add a comment |
they are not comparable if we dont have FP or FN?
– Saha
Dec 28 '18 at 12:07
You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice.
– David
Dec 29 '18 at 8:16
they are not comparable if we dont have FP or FN?
– Saha
Dec 28 '18 at 12:07
they are not comparable if we dont have FP or FN?
– Saha
Dec 28 '18 at 12:07
You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice.
– David
Dec 29 '18 at 8:16
You do not need direct access to FN or FN values. Precision and recall only varies in a part of their fraction formulas which includes FN or FP, so your displayed values reflect their relationship. What Nga Dao and I mean is that your choice depends on the impact of false positives or false negatives in your system. Nga Dao example is quite nice.
– David
Dec 29 '18 at 8:16
add a comment |
It depends on your application. Assume that you design a classifier model to predict whether a person has cancer. If you wanna say confidently that a person has cancer, you probably prefer a classifier with high precision.
On the other hand, if you want to make sure all people with cancer will be caught, you probably prefer a classifier with high recall.
answered Dec 28 '18 at 13:38
Nga Dao
1495
add a comment |
It depends on your application. Assume that you design a classifier model to predict whether a person has cancer. If you wanna say confidently that a person has cancer, you probably prefer a classifier with high precision.
On the other hand, if you want to make sure all people with cancer will be caught, you probably prefer a classifier with high recall.
answered Dec 28 '18 at 13:38
Nga Dao
1495
add a comment |
It depends on your application. Assume that you design a classifier model to predict whether a person has cancer. If you wanna say confidently that a person has cancer, you probably prefer a classifier with high precision.
On the other hand, if you want to make sure all people with cancer will be caught, you probably prefer a classifier with high recall.
answered Dec 28 '18 at 13:38
Nga Dao
1495
It depends on your application. Assume that you design a classifier model to predict whether a person has cancer. If you wanna say confidently that a person has cancer, you probably prefer a classifier with high precision.
On the other hand, if you want to make sure all people with cancer will be caught, you probably prefer a classifier with high recall.
answered Dec 28 '18 at 13:38
Nga Dao
1495
answered Dec 28 '18 at 13:38
Nga Dao
1495
answered Dec 28 '18 at 13:38
Nga Dao
1495
answered Dec 28 '18 at 13:38
Nga Dao
1495
1495
add a comment |
add a comment |
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Why is accuracy not the best measure for assessing classification models? Everything in that thread applies equally to F1, recall and precision. See also Classification probability threshold.
– Stephan Kolassa
Dec 28 '18 at 13:31