I have layoutlm model train on
Funds dataset and I need to train
On docbank dataset
Related
Why is the accuracy on train dataset not always 100% while we use the same dataset to train the model?
Though tree-based ML algorithms give us 100% accuracy on train dataset many times, but why is this not happening every time. I know this results in overfitting but why not 100% accuracy every time on the dataset using which our model is trained?
I have a problem with xgboost predictions.
I have trained a xgboost model for my regression problem in python but when max_depth parameter is given different than default value, some of predictions changes if it is predicted again with the same model.
So far I tried changing basic parameters like learning rate, reg_lamda and so on but only max_depth causes randomness in predictions for same data.
I am new to sktime and time series forecasting. I am using the following Kaggle dataset to forecast sales for different stores:
I have created x_train and y_train datasets as follows:
I want to train a model using date, store, and item features and make predictions. I am now looking for an example, where I can use SkTime to train Arima, Prophet, and LightGBM model on the x_train dataset. After training, I will like to make predictions using x_test dataset.
x_test dataset has the same structure as x_train i.e. having date, store, and item columns.
Any help or guidance will be really appreciated.
Thank
I was training a dataset, contain 3000 images using resnet and lstm, but the model just got overfiting in epoch 5. Does the small dataset affect the number of epoch?
What are the factors that affect the number of epochs for data trained using transfer learning? Is there a paper that discusses it?
I am using class weights for multiclass classification using sklearn's compute_weight function and pytorch for training the model. For to compute the class weight, whether we need to use all data (training, validation and test) or only training set data for calculating the class weights. Thanks
When training your model you can only assume training data is available for you.
Estimating the class_weights is part of the training -- it defines your loss function.