Questions tagged [f1score]
The f1score tag has no usage guidance.
55 questions
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Why Do Two NER Models with Different Training Data Yield Identical Results?
I've developed several versions of an NER model using spaCy to identify singers in file names. Despite making significant updates to the training corpus—comprising approximately 13,000 entries—and ...
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Does f1 score evaluate only the model or does it also enable us to observe and evaluate the data?
I have a dataset. This dataset consists of the data that the actual picture that needs to be drawn, that is, the 100-point graded paper, and the similarity between 100 and 0 points graded pictures ...
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Why the f1 score on validation dataset significantly higher than f1 score on testing dataset?
I'm using a TensorFlow model that look likes this:
...
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PR-AUC vs F1 vs Balanced Accuracy
I'm trying to create a Random Forest Classifier for selecting ~ 700 features.
I have a highly imbalanced dataset to select features from. There are significantly fewer positive cases (1%) compared ...
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What cost optimisation problem is solved by F score?
I know the general expression of the F1-score:
$$F1 = \frac{precision * recall}{precision + recall}$$
And its $F_{beta}$ variants (see: https://en.wikipedia.org/wiki/F-score):
$$F_{beta} = (1+\beta^2) ...
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Which analog of F1 score metrics can I use in this case?
I am training a cnn segmentation model and I need some analog of F1 score
So, we have GT as red rectangles (called "red") and Pred as blue rectangles (called "blue").
It is clear ...
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When is Recall@k useful for a classifier with softmax-like output?
If a 3-class classifier returns a length-3 vector of probabilities, e.g. [0.1, 0.85, 0.05] for classes A, B, and C respectively (strongly indicating B), does it ...
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Correctness of derivation for binary F1 variance for F1 confidence intervals
I'm developing a python library for confidence intervals for common accuracy metrics, with both analytic and bootstrap computations.
Following this paper, I implemented the Macro and Micro F1 scores ...
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How to explain relative difference between macro-AUC and macro-F1 in a multiclass classification problem?
I recently published a paper in which the result of a supervised model is the following.
All the metrics are macro-averaged.
I have been asked to comment on the gap between the AUC and the other ...
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4/96 imbalanced but all metrics above .95
I'm working with some severely imbalanced dataset where my 1 class represents 4% of the data in a binary classification problem.
I have about 10M rows and developed a model that outputs +.95 in ...
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Feature Engineer each class separately in Binary Classification
I have an imbalanced tabular dataset, my problem is a binary classification. The dataset is strongly imbalanced so I have performed oversampling, but it did not solve the issue, you can find the ...
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output F1-score instead of Accuracy
I have the code below outputting the accuracy. How can I output the F1-score instead?
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233
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Scikit learn ComplementNB is outputting NaN for scores
I have an unbalanced binary dataset with 23 features, 92000 rows are labeled 0, and 207,000 rows are labeled 1.
I trained models on this dataset such as GaussianNB, DecisionTreeClassifier, and a few ...
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Difference between the different measurement metric [closed]
Can someone explain what each of these mean? both in simple terms and in terms of TP, TN, FP, FN?
Also are there any other common metrics that I am missing?
F-measure or F-score
Recall
Precision
...
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981
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Is it correct to train and validate the model on F1-score metrics?
I am trying to do experiments on multiple data sets. Some are more imbalanced than others. Now, in order to assure fair reporting, we compute F1-Score on test data. In most machine learning models, we ...
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Overfitting? Is it ok, if I've met my desired threshold?
I've trained a lightgbm classification model, selected features, and tuned the hyperparameters all to obtain a model that appears to work well.
When I've come to evaluate it on an out of bag selection ...
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How F1 score is good with unbalanced dataset
I have read around on this site that it's recommended to use F1 score if the dataset is imbalanced and if you want to seek a balance between recall and precession. Could you please explain how F1 can ...
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NameError: name 'model' is not defined Keras with f1_score
I'm having a problem with my Keras model, in the .compile() I use accuracy, loss, precision, recall and AUC, but also I need f1_score, due to Keras doesn´t include f1_score, I tried to calculate by ...
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problem with using f1 score with a multi class and imbalanced dataset - (lstm , keras)
I'm trying to use f1 score because my dataset is imbalanced. I already tried this code but the problem is that val_f1_score is always equal to 1. I don't know if I did it correctly or not. my X_train ...
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Perfect scores for multiclass classification
I am working on a multiclass classification problem with 3 (1, 2, 3) classes being perfectly distributed. (70 instances of each class resulting in (210, 8) dataframe). Now my data has all the 3 ...
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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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394
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How to measure multi-label multi-class accuracy
I have a model that has multi-label multi-class targets
Example
Age
Height
Weight
Mark
Distance
Red
Yellow
Green
Blue
Black
White
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How to improve my f1 score in stories analyze
I got an assignment to build a model that identify the gender of the text writer.
The assignment score will determine by my model f1_score, to get the maximum points, T need it will be at least 0.7.
I'...
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Making an ensemble model for high F1 score
I presently have 2 algorithms that have a numerical output. Using a threshold of 0.9, I get the classification output. Let's say they are:
P (high precision, low recall)
R (high recall, low precision)...
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Accuracy on Validation and Test set, Overfit?
Just a quick question: I am building an ML model right now, however, I am receiving very similar (72.2 and 72.4 for example)% for both Accuracy and F1-Score on my Validation Dataset and my unseen Test ...
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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. Pitch estimation is done by inputting spectrogram to CNN model and make the CNN predict pitch sequence (250 pitch values per ...
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772
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How to compute f1_score for multiclass multilabel classification
I have used one hot encoder [1,0,0][0,1,0][0,0,1] for my functional classification model.
The predicted probabilities for test data ...
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Warning when plotting confusion matrix with all sample of one class
I have two arrays: the first one with all the correct labels (they are all set to zero since each sample belong to the same class) and another one with all the labels predicted by my neural network. ...
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Which F1-score is used for the semantic segmentation tasks?
I read some papers about state-of-the-art semantic segmentation models and in all of them, authors use for comparison F1-score metric, but they did not write whether they use the "micro" or &...
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scikit-learn classification report's f1 accuracy?
When I run scikit-learn classification_report() on my 2-class y and yhat, I get the ...
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What is the appropriate statistical significance test for multi-class classification?
I have a multi-class classification problem. I am primarily using macro-average F1 measure to evaluate the performance of models and want to verify if the results are statistically significant. I have ...
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Too high performances on a classification problem
I have a .json file as dataset of the type:
and I am working on a classification problem in which I have to predict 4 classes, which are rhe semantic. I have worked through the problem, and after ...
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Is an $F_1$ score of 0.1 always bad?
I'm currently building a model to predict early mortgage delinquency (60+ days delinquent within 2 years of origination) for loans originating in 2018Q1. I will eventually train out-of-time (on loans ...
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Model to choose with Cross Validation or not?
I made different tests on an imbalanced dataset and got these results:
Model 1 = train test validation split + Cross Validation(cv=10) --> f1'micro' 0,95
Model 2 = train test split + smote method ...
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how print f1-score with scikit´s accuracy_score or accuracy of confusion_matrix?
I would like to print the f1-score. I got confused about the wording f1-accuracy score and accuracy score. What is the difference of these 2 scikit-learn metrics and how can I print the f1-score out ...
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Multiclass Classification and log_loss
I hope I can make this clear with few lines of code/explanation.
I've a 16K list of texts, labelled over 30 different classes that were ran through different classifiers; my Prediction and the Ground ...
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Accuracy is lower than f1-score for imbalanced data
For a binary classification, I have a dataset with 55% negative label and 45% positive labels.
The results of the classifier shows that the accuracy is lower than the f1-score.
Does that mean that the ...
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Selecting threshold for F1 Score
When selecting a probability threshold to maximize the F1 score prior to deploying a model (based on the precision-recall curve), should the threshold be selected based on the training or holdout ...
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How to calculate the evaluation metrics (i.e., F1 score) in leave one subject out cv when a subject belongs to single class only
I have dataset of 10 subjects. the dataset has 4 classess. 0,1,2 and 3. The distribution of classes are not same. For example subject 1 does not have 1,2 and 3. It belongs to zeros class. currently ...
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Is it conscientious to use a threshold for a model output in order to play on the recall and precision?
I have just finished reading an article about the F1 score, recall and precision. Everything was clear except the fact that the author, in his example (see https://towardsdatascience.com/beyond-...
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Balanced Accuracy vs. F1 Score
I've read plenty of online posts with clear explanations about the difference between accuracy and F1 score in a binary classification context. However, when I came across the concept of balanced ...
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How can I calculate the F1 score using Mask RCNN?
I customized the "https://github.com/matterport/Mask_RCNN.git" repository to train with my own data set, for object detection, ignoring the mask segmentation part. Now I am evaluating my results, I ...
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Confusion matrix of UNET image sgemenation model
I have used Unet model for image segmentation. I have used RGB images and corresponding image masks and at output i got corresponding region of interest. Now i want to find confusion matrix of this ...
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Calculating the F score of Object Detection of Mask RCNN
I am using Detectron2 Mask RCNN for an object detection problem. The images consist of cells that are very close to each other. I can not use mAP as a performance measure since the annotations are a ...
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Can the F1 score be equal to zero?
As it is mentioned in the F1 score Wikipedia, 'F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0'.
What is the worst condition that was mentioned?
Even if we ...
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What is evaluation metric for two sets? [closed]
I've two sets one is ground truth and other is output of my machine learning models. Assume my groundtruth set is A={1,2,3,4,5} and output of machine learning model is B={3,4,5,6,7,8}. One way I can ...
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Monotonicity of Jaccard and Dice in multilabel datasets
I understand that Jaccard and Dice follow a monotonic relation on binary datasets because the two are related as $J = {S \over {(2 - S)}}$, and I guess this would be the case when micro-average is ...
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Different definitions of Macro F1 score, which one is used in Scikit-learn?
In this article Macro F1 and Macro F1 two different definitions of the F1 used in the literature are demonstrated.
The first F1 score is computed such as:
F1 scores are computed for each class ...
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Multiclass imbalanced classification
I have a dataset with the target variable having 3 classes. Value counts of Target variable are as follows:
...