Questions tagged [f1]

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11 views

How to calculate F1, Precision, and Recall for Multi-Label Multi-Classification

I have a predictive model as follows Sample1 Sample2 Sample3 Sample4 Red Yellow Blue Green White Black Orange 65 21 55 40 0 0 1 0 1 0 0 31 40 44 30 0 0 0 0 0 0 0 33 44 56 66 1 0 0 1 0 0 1 63 77 ...
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1answer
35 views

Model accuracy versus F1

When training a model (classifier) in TensorFlow, an accuracy value is returned. What is the interpretation of an accuracy of, say, ...
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0answers
11 views

Is there a F1-score for the negative class?

I am analysing some data and calculated the $F1 = \frac{2TP}{2TP + FP + FN}$ I'd like to know if there is a name to a negative-class based F-score, something like $F0 = \frac{2TN}{2TN + FP + FN} = 2 \...
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0answers
24 views

How to calculate chance-level f1, ROC-AUC, PR-AUC for imbalanced dataset

I have an imbalance dataset (60% class 1, 40% class 0). I trained a model and got accuracy, f1, ROC-AUC and PR-AUC. I want to compare them to chance-level performance. obviously chance-level of acc if ...
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2answers
24 views

What is the F1 Score for my prediction when all values are negative?

I have built a model that gives me classification of some cases here is a comparison between Actual and Prediction ...
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0answers
8 views

How to evaluate F1 performances using sklearn for imbalanced multiclass classifier, with different weighting for train and test sets?

I have an imbalanced dataset. The number of occurrences\weight of each class in the test and train sets is different. I wish to use the sklearn implementation of the F1 score for the evaluation. I am ...
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1answer
24 views

F1 Score is giving good value in imbalanced dataset

If I have an imbalanced dataset that consists of 90% positive points and 10% negative points. Now I created a "dumb" model which always predicts every point as a positive point. The ...
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0answers
18 views

Continuously occurring true negatives

How can I handle discrete events in a continuous time stream in the context of an F1 metric? To give an example, let's say the Earthquake Forecasting Bureau would report the following for their ...
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1answer
77 views

Question answering bot: EM>F1, does it make sense?

I am fine-tuning a Question Answering bot starting from a pre-trained model from HuggingFace repo. The dataset I am using for the fine-tuning has a lot of empty answers. So, after the fine tuning, ...
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0answers
15 views

Average Precision vs average F1

Average precision computes the area under the recall-precision curve by the trapezoidal rule (or midpoint rule). However, we could also compute the F1 score for every threshold and then take the ...
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1answer
23 views

hyperparameter search with unknown test set distribution

I'm training a 3-class neural network classifier (conv layers and softmax at the end, nothing special). Let's say, in the test set I will have N1 examples of the 1st class, N2 examples of the 2nd ...
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1answer
80 views

Why use harmonic mean for precision and recall (f1 score) instead of just the product of precision and recall?

General question here, I understand the purpose of using the harmonic mean to generate the f1 score for model evaluation. I'm not exactly sure though why we don't just take the product of precision ...
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1answer
372 views

On which set (train/val/test) do people calculate F1 score, precision and recall?

This may be a stupid question, but when I was looking at the definition of precision/recall etc. it was not mentioned anywhere which set (training/validation/test) this metric should be calculated ...
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0answers
16 views

How to explain a relationship between Accuracy and F1 Score / F-Measure?

I am building a CNN model for pitch estimation using a song recording. For the evaluation metrics, I am using Accuracy and ...
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0answers
288 views

f1-score of imbalanced data within k fold cross validation

I am trying to find the f1 score, precision, recall of a highly imbalanced dataset. I would like to use k-fold cross validation approach. I followed the procedure: create arrays to store testing data ...
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0answers
45 views

How to calculate f1 score for anomaly detection with one class (Anomaly or No Anomaly)? What is seen as the true positive?

How do we calculate the f1 score in anomaly detection (using a One-Class-Support Vector Machine(OC-SVM))? I am not sure what is considered as a true positive? Is it if I predict an anomaly and the ...
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1answer
32 views

Harmonic is used in F1 score because it is a conservative metric: How does it help being conservative?

I was reading Jurafsky 3rd edition, page 12-13 chapter 4 Can you explain why is it good to weigh more the smaller of the two items namely $\frac{1}{Precision}$ or $\frac{1}{Recall}$? Here is the link ...
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1answer
104 views

Are Jaccard score and F1 score monotonically related?

I have compared the rankings obtained by comparing 10+ classifiers with this two metrics: Jaccard score F1 score They show a perfect correlation. This results holds on 50+ datasets. When comparing ...
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0answers
8 views

What ratio of skewness is typically a switch from accuracy to f1

I have a dataset where the targets are binary with the ratio 2:1 What ratio of skewness is typically a switch for a classification metrics accuracy vs f1? So far I think I know: If I would have 1:99 ...
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0answers
401 views

Are F1 score and Dice coefficient computed in same way or different way in image segmentation (two class segmentation)?

On page 8 of the paper An automatic nuclei segmentationmethod based on deep convolutional neuralnetworks for histopathology images, the authors show performance of their deep model on test sets. They ...
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1answer
257 views

F1 weighted vs. Log loss in SciKit learn RandomSearchCV

I am sorry to ask another question regarding this topic but I am still puzzled about the following: When I use 'F1_weighted' as my scoring argument in a ...
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0answers
7 views

Performance Metrics on Within Subjects Study

I ran a simple yes or no within-subject experiment. i.e there were 4 conditions, and each participant answered a single question for a subset of images per condition. My question is how would I ...
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1answer
425 views

F1 score, PR or ROC curve for regression

Due to my background as a pure biologist, I've been struggling with the comment acquired from a reviewer about the accuracy test used in my regression study. While I stick to MSE, MAE and R2 as the ...
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1answer
28 views

Is it possible for a binary classifier to have lower accuracy, macrof1 and binaryf1 but higher ROC AUC? [duplicate]

I've got the results of two classifiers based on 5 different splits of training and testing sets. Their mean and std of the results are as follow: Method-------Accuracy -- MacroF1 -- BinaryF1---- ROC ...
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0answers
126 views

Imbalanced classification what is good F1 score?

For imbalanced classification (say 85:15), what is good value of F1 score? An answer https://stats.stackexchange.com/a/217343/285091 says "Experiments indicate that the sweet spot here is around ...
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2answers
171 views

Classifier can predict time series 1 day in advance, but not more. Why?

To ask the question more precisely: when doing Time Series classification, I observe the classifier prediction is good if test data directly follows (in chronology) the train data. But when the train ...
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0answers
534 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 ...
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1answer
1k 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
12 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
47 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
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1answer
292 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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0answers
762 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
580 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 ...
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1answer
23 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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0answers
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
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1answer
498 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
1k 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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2answers
67 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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0answers
99 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 ...
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1answer
108 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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2answers
2k 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
2k 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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2answers
381 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 ...
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0answers
544 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 ...
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2answers
375 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 ...