Can the Precision, Recall and F1 be the same value?
I am working on an ML classification problem and I'm computing the Precision, Recall and F1 using the sklearn library's following import and respective code as shown below.
from sklearn.metrics import precision_recall_fscore_support
print(precision_recall_fscore_support(y_test, prob_pos, average='weighted'))
Results
0.8806451612903226, 0.8806451612903226, 0.8806451612903226
Is there a possibility to get the same value for all 3, the precision, recall and F1 for an ML classification problem?
Yes, this is possible. Let's assume binary classification with
The trivial solution to Pr = Re = F1 is TP = 0. So we know precision, recall and F1 can have the same value in general. Now, this does not apply to your specific result. If we solve the system of equations, we find another solution: FP = FN. So, if the number of false positives is the same as the number of false negatives, all three metrics have identical values.
For multiclass classification problems we have
If Pr = Re, again all three metrics are identical.
- How many images(minimum) should be there in each classes for training YOLO?
- Why do neural networks work so well?
- Will larger batch size make computation time less in machine learning?
- Why KL divergence is negative in Pytorch?
- Creating a voice identification system using machine learning
- Forward pass with all samples
- fit method in sklearn
- Calibrating Probabilities in lightgbm or XGBoost
- Implementation of F1-score, IOU and Dice Score
- Is it ok to have the training history very similar to the validation history?
- How to understand Shapley value for binary classification problem?
- Stochastic Gradient Descent for Logistic Regression always returns a cost of Inf and weight vector never gets any closer
- Data format for Libsvm SVR training in Matlab
- Why shouldn't we use multiple activation functions in the same layer?
- How to implement a butterworth filter
- weka java api stringtovector exception
- Predict training data in sklearn
- Text classification with weka
- How can i apply feature reduction methods in Weka?
- 'super' object has no attribute '__sklearn_tags__'
- lightgbm.cv: cvbooster.best_iteration always returns -1
- Wrong detection from yolov5 model
- torchrl: Using SyncDataCollector with a custom pytorch dqn
- OCR Preprocessing for Oman License Plates - Issues with Alphabet Recognition
- Training difference between LightGBM API and Sklearn API
- Why can't I wrap LGBM?
- Why RAG is slower than LLM?
- Azure AutoML Image Classification Job
- What's the best way to use a sklearn feature selector in a grid search, to evaluate the usefulness of all features?
- AdaBoostClassifier: Perfect Metrics with test_size=0.25, but Inconsistent Samples Error for Other Values