query: Is it possible to refit after cross-validation and continue to train the model for better accuracy?
positive_passages.docid: 11067
positive_passages.text: How to choose a predictive model after k-fold cross-validation?
negative_passages.docid: 115455
negative_passages.text: What is the correct way to use train, validation and test set when comparing classifiers?
negative_passages.docid: 48916
negative_passages.text: Determing two Nonlinear model fits with 95% confidence bands from one data set
negative_passages.docid: 34923
negative_passages.text: How does leave-one-out cross-validation work? How to select the final model out of $n$ different models?
negative_passages.docid: 54302
negative_passages.text: Cross-Validation in plain english?
negative_passages.docid: 117722
negative_passages.text: Interplay between early stopping and cross validation
negative_passages.docid: 60749
negative_passages.text: What could be the reasons that making validation loss jumping up and down?
negative_passages.docid: 96296
negative_passages.text: Imputation before or after splitting into train and test?
negative_passages.docid: 25409
negative_passages.text: Why does k-fold cross validation generate an MSE estimator that has higher bias, but lower variance then leave-one-out cross-validation?
negative_passages.docid: 132334
negative_passages.text: Is it okay to use cross entropy loss function with soft labels?
negative_passages.docid: 120234
negative_passages.text: What would I prefer - an over-fitted model or a less accurate model?
negative_passages.docid: 63049
negative_passages.text: Train loss and accuracy
negative_passages.docid: 108205
negative_passages.text: Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
negative_passages.docid: 87457
negative_passages.text: With k-fold cross-validation, do you average all $k$ models to build the final model?
negative_passages.docid: 107720
negative_passages.text: Resampling / simulation methods: monte carlo, bootstrapping, jackknifing, cross-validation, randomization tests, and permutation tests
negative_passages.docid: 58047
negative_passages.text: Decision Trees and Regression - Can predicted values be outside range of training data?
negative_passages.docid: 110802
negative_passages.text: Model selection with low N?