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 yprob = model.predict(testX) gives me : yprob = array([[0.18120882, 0.5803128 , 0.22847839], [0.0101245 , 0.12861261, 0.9612609 ], [0.16332535, 0.4925239 , 0.35415074], ..., [0.9931931 , 0.09328955, 0.01351734], [0.48841736, 0.25034943, 0.16123319], [0.3807928, 0.42698202, 0.27493873]], dtype=float32) I would like to compute the Accuracy, F1 score and the confusion matrix from this. The sequential api offers a predict_classes function to do it. yclasses = model.predict_classes(testX) and …
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 "macro" version of it. Does anyone know which F1-score is used to describe the segmentation results and why it is so obvious that authors do not define it in papers? Sample papers: https://arxiv.org/pdf/1709.00201.pdf https://arxiv.org/pdf/1511.00561.pdf
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) Individually, they have poor F-1 scores. Is the naive way of creating a classifier C as: C(*) = x.P(*) + (1-x).R(*) And optimizing for x and threshold a good approach to improve the F-1 score? Or is there some alternate approach I must try. Note: I …
I have the code below outputting the accuracy. How can I output the F1-score instead? clf.fit(data_train,target_train) preds = clf.predict(data_test) # accuracy for the current fold only r2score = clf.score(data_test,target_test)
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 more classifiers from scikit learn, and they all work fine. I want to run ComplementNB on this dataset, but when i do so, all the scores are coming out as NaN. Below is my code: from sklearn.naive_bayes import ComplementNB features = [ # Chest accelerometer sensor 'chest_accel_x', …
Just a quick question, I am building a 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 Set respectively. This is occuring on most of the baseline models I have produced for my problem right now. Is this showing that my model is completely overfitting or just acting completely random and getting lucky. Thanks
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 accuracy, explained e.g. in the following image (source) or in this scikit-learn page, I was a bit puzzled as I was trying to compare it with F1 score. I know that it is probably impossible to establish which is better between balanced accuracy and F1 score as it could …
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 Accuracy
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 train and validate the model via accuracy measure metric. However, this time, I decided to train and validate the model on an F1-score metric measure. Technically, there should be no problems, in my opinion. However, I am wondering if this is the correct approach to …
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 of data, it appears to be slightly overfit to the training data. CV mean F1 score. = .80 OOB F1 score = .77 For me this appears to be an acceptable tolerance. For my chosen requirements an out of bag score of .77 is perfectly acceptable. …
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 be useful in terms of imbalanced dataset?
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 model is learning the negative instances much better than the positive ones? Does that even make sense, to have accuracy less than the 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 myself but I get this error NameError: name 'model' is not defined, here's my code: def residual_network_1d(input_shape): n_feature_maps = 64 input_layer = keras.layers.Input(input_shape) # BLOCK 1 conv_x = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=8, padding='same')(input_layer) ... # FINAL gap_layer = keras.layers.GlobalAveragePooling1D()(output_block_3) output_layer = keras.layers.Dense(27, activation='softmax')(gap_layer) model = keras.models.Model(inputs=input_layer, outputs=output_layer) …
I was trying to generate my own F1 metric, however I am wondering why I only get 10 rows for my prediction in the data parameter. Can somebody please clarify were it doesn't return me all predictions and obs made and how the F1_score is able to predict from 10 rows? Here they code: set.seed(346) dat <- twoClassSim(200) ## See https://topepo.github.io/caret/model-training-and-tuning.html#metrics f1 <- function(data, lev = NULL, model = NULL) { print(data) f1_val <- F1_Score(y_pred = data$pred, y_true = data$obs, …
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, when I'm evaluating the dataset by using the model just created, I find that the EM score is (much) higher than the F1 score. (I know that I must not use the same dataset for training and evaluation, it was just a quick test to see …
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 data has a shape of (50000,30,10) and Y_train data has a shape of (50000,). I have 3 classes: 0, 1 and 2. this is my code so far: maximum_epochs = 40 early_stop_epochs= 60 learning_rate_epochs = 30 maximum_time = 8*60*60 model = …
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 bit off from the original location and the prediction is actually more accurate and when I use mAP it will give bad results. Generally, each cell is 30 pixels apart and if the predicted and the actual are less then 30 pixels apart …
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 classes distributed in order i.e first 70 instances are class1, next 70 instances are class 2 and last 70 instances are class 3. I know that this kind of distribution will lead to good score on train set but poor score on test set as the …
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 consider the case of: either precision or recall is 0. The whole F1-score value becomes undefined. Because when either precision or recall is to be 0, true postives should be 0. When the true positives value becomes 0, both the precision and recall become …