I try to train and test several scikit-learn models and attempt to print off the accuracy. Only some of these models work, others fail with th
"after converting the dataset to the tfrecord file format, I tried to train the model I created with it, but I couldn't convert it to the input format suitable
I am currently planning on training a binary image classification model. The images I want to train on are the difference between two original pictures. In ot
I am running a logistic regression in R and extracting the predicted probabilities for a test data of about 15,000 rows using predict(modelglm, test_data, type
I used the following code: plt.figure(figsize = (7, 7)) plt.boxplot([totalP['poly'], totalP['rbf'], totalP['linear'], totalP['gf']]) plt.xticks(np.arange(1, 5),
I have a dataset that consists of images and associated descriptions. I've split these into two separate datasets with their own classifiers (visual and textual
I'm trying to use VGG16 for ** 5 classes data set**. I've already added 5 new layers to adjust the output for logit as 5. model = models.vgg16(pretrained=True)
I want to calculate the F1 score of my models. But I receive a warning and get a 0.0 F1-score and I don't know what to do. here is the source code: def model_
I have a multiclass problem where an image can be one of three classes (Masked, UnMasked, Hybrid). I am using image_dataset_from_directory from keras preprocess