Questions tagged [f1]
The f1 tag has no usage guidance.
21
questions
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19 views
What's a range of good F1 scores?
I have watched a lot of videos on machine learning and in terms of F1 scores, all are different. One video says that an F1 score of .8 is bad, but another says an F1 score of .4 is excellent. What's ...
0
votes
1answer
22 views
Should I balance the classifier train/test set, if metrics is Precision/Recall (F1 score)?
I want to train a classifier on an unbalanced data set. Proportions of classes C0/C1 are 65/35. Importantly, the success metrics is F1_score. In other words, the proper classification of class 1 (...
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0answers
11 views
How to verify that one ML-classifier is better then the other using the same train and test data set without cross-validation?
I have compared 5 methods (ML classifiers) on the same data set. These methods are 5 different types of neural networks. Each is trained on the training set and evaluated with precision, recall and f1-...
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1answer
25 views
PPV vs Sensitivity, they look the same!
I am looking at the equation PPV and Sensitivity
and I got this
PPV = TP / (TF+FN)
and
Sensitivity = TP / (TF+FN)
Which ...
1
vote
1answer
80 views
F1 score macro-average
In this question I'll differentiate by using lower-case for class-wise scores, e.g. prec, rec, f1, which would be vectors, and the aggregate macro-average Prec, Rec, F1. My formulae below are written ...
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36 views
Maximize F1 Score for an imbalanced data and multi-class classification
I'm dealing with an multiclass classification problem. The data is textual and too imbalanced. I see that the models that i'm building using the character level or word level grams are always giving ...
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0answers
43 views
Issue with training on classification metrics other than accuracy (using R and caret)
I have a binary classification problem with two classes 0 and 1. For training an XGBoost classification model, I apply a balanced data set (50% 0's, 50% 1's).
In reality, 1's are much more abundant ...
1
vote
1answer
18 views
what is the correct interpretation of precision, recall and F1 in R?
Im using R and i had some cases of NAs for F1 when there is NA for precision and 0 for recall and also when both are 0, i also noticed that with both 0 i had f1 as Nan. So im not sure how to interpret ...
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14 views
Why would my additional information harm my prediction score but improve ROC and F-1?
I'm trying to predict the primary crime type on a given location using the Chicago crime dataset.
Stripping out all the provided features to just:
Location Description Encoded (The location ...
4
votes
1answer
71 views
Use F1 or maximum F1 for model comparisons?
I am comparing a ML classifier to a bunch of other benchmark F1 classifiers by F1 scores. By AUPRC, my classifier does worse than other benchmark methods. When I compared F1 score, however, I got a ...
1
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1answer
136 views
Can F1 score be equal to zero?
As it is mentioned in F1 score Wikipedia that 'F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0'.
What is the worst condition that was mentioned?
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60 views
Measuring performance of classifiers with different/extra classes
I'm not sure where to post this, or how best to explain, so please bear with the bullet point approach below!
I have created a decision tree using "perfect" labelled data which works 100%.
I have a ...
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2answers
19 views
Removing Multi-Collinearity Reduces F1 score
I was trying to build a classification model and I found that the features were highly correlated.
I tried run a random forest model on the features and got an F1 score of 0.44 but when I removed the ...
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vote
0answers
27 views
sklearn f1_score=weighted not matching sample_weight specification
I am trying to figure out exactly what this is doing:
sklearn.metrics.f1_score(y_pred, y_test, sample_weight=[...])
Numerically it simply does not seem to be ...
0
votes
1answer
50 views
High AUC, low f1, SVM threshold for an unbalanced problem
I have a very unbalanced binary classification problem (positive class: 0.2%).
I need to evaluate it using f1 of the positive class.
Now, I'm doing some baselines using an SVM. What I get is a ...
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0answers
20 views
How to back out individual confusion matrix values from scalar measures like sensitivity and specificity?
Consider a standard 2X2 Binary Classification Matrix:
TP | FP
FN | TN
From which we can derive sensitivity and specificity, and other measures.
Now, let's assume we have ONLY output measures: ...
2
votes
1answer
464 views
F1-Score in a multilabel classification paper: is macro, weighted or micro F1-used?
I read this paper on a multilabel classification task. The authors evaluate their models on F1-Score but the do not mention if this is the macro, micro or weighted F1-Score.
They only mention:
We ...
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0answers
343 views
In XGBoost with a f1_score, is the iteration with a lower or higher score the better iteration?
In the following XGBoost script the output states iteration 0 with score 0.0047 is the best score. I would expect iteration 10 with score 0.01335 to be the better score?
Output
...
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1answer
67 views
Why does the 'weighted' f1-score result in a score not between precision and recall?
On the F1 score sklearn page there's a section that explains each of the options for the average parameter. Under the weighted option, it says: "it can result in an F-score that is not between ...
2
votes
0answers
150 views
What is the difference of “normal” F1 and macro average F1 score with binary classification
Please note that I always talk about binary classification here. I do not speak about multi class classification.
In case of unbalanced binary datasets it is a good practice to use F1 score. While ...
2
votes
2answers
132 views
Reporting F1 Scores
I have a question with regard to the proper way to report F1 scores. Say I am comparing two algorithms one with F1 score of 0.71 and the other of 0.82.
Is it correct to say:
"Algorithm 1 obtained an ...
